US10303181B1 - Self-driving vehicle systems and methods - Google Patents

Self-driving vehicle systems and methods Download PDF

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US10303181B1
US10303181B1 US16/205,013 US201816205013A US10303181B1 US 10303181 B1 US10303181 B1 US 10303181B1 US 201816205013 A US201816205013 A US 201816205013A US 10303181 B1 US10303181 B1 US 10303181B1
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location
area
computing device
remote computing
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Eric John Wengreen
Wesley Edward Schwie
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Drivent LLC
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Drivent LLC
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Assigned to SCHWIE, WESLEY EDWARD, WENGREEN, ERIC JOHN reassignment SCHWIE, WESLEY EDWARD ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: DRIVENT TECHNOLOGIES INC.
Assigned to DRIVENT LLC reassignment DRIVENT LLC ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: SCHWIE, WESLEY EDWARD, WENGREEN, ERIC JOHN
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    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02Control of position or course in two dimensions
    • G05D1/021Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0287Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
    • G05D1/0291Fleet control
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in preceding groups
    • G01C21/26Navigation; Navigational instruments not provided for in preceding groups specially adapted for navigation in a road network
    • G01C21/34Route searching; Route guidance
    • G01C21/3407Route searching; Route guidance specially adapted for specific applications
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42Determining position
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/0011Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot associated with a remote control arrangement
    • G05D1/0027Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot associated with a remote control arrangement involving a plurality of vehicles, e.g. fleet or convoy travelling
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in preceding groups
    • G01C21/26Navigation; Navigational instruments not provided for in preceding groups specially adapted for navigation in a road network
    • G01C21/34Route searching; Route guidance
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/0088Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D2201/00Application
    • G05D2201/02Control of position of land vehicles
    • G05D2201/0212Driverless passenger transport vehicle
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D2201/00Application
    • G05D2201/02Control of position of land vehicles
    • G05D2201/0213Road vehicle, e.g. car or truck

Abstract

A vehicle management system can include a self-driving vehicle and a computer system that is communicatively coupled with a remote computing device of a rider. The vehicle management system can include a location tracking system configured to receive a first location data indicative of a drop-off location where the self-driving vehicle dropped off the rider. The location tracking system can receive a second location data indicative of locations of the remote computing device during a period from after the self-driving vehicle fleet drops off the rider to before the self-driving vehicle fleet picks up the rider. The computer system can be configured to prompt the self-driving vehicle to drive to an area within 100 feet of the drop-off location to pick up the rider in response to determining that the second location data is indicative of the remote computing device having returned to the area after being dropped off.

Description

BACKGROUND Field

Various embodiments disclosed herein relate to vehicles. Certain embodiments relate to self-driving vehicles.

Description of Related Art

According to the National Highway Traffic Safety Administration, 37,133 people were killed in vehicle crashes in 2017 in the United States. Most vehicle deaths were caused by human errors. Self-driving vehicles can eliminate nearly all driving errors, which will save tens of thousands of lives per year. Self-driving vehicles, however, previously struggled to perform tasks required to transport people in an efficient manner. Simply knowing how to steer, brake, and accelerate is insufficient. Thus, there is a need for systems and methods that enable self-driving vehicles to perform actions required to transport people in an efficient manner.

SUMMARY

The ability of self-driving vehicles to save lives is so impressive that society has a moral imperative to develop self-driving technology such that it can be widely adopted. Self-driving vehicles will save tens of thousands of lives per year. The majority of vehicle-related deaths are caused by driver error. Tests have shown that self-driving vehicles nearly eliminate self-inflicted accidents (although they are not immune to accidents caused by human drivers of other vehicles). Self-driving vehicles can have unlimited attention spans and can process complex sensor data nearly instantaneously.

In some embodiments, a vehicle management system comprises a self-driving vehicle fleet having at least one of a first self-driving vehicle and a second self-driving vehicle. The self-driving vehicle fleet can be configured to transport a rider. (In some embodiments, the self-driving vehicle fleet only has one vehicle. In some embodiments, the self-driving vehicle fleet has hundreds of vehicles.) The first self-driving vehicle can drop off and then later pick up the rider. The second self-driving vehicle can drop off and then later pick up the rider. The first self-driving vehicle can drop off the rider, and then the second self-driving vehicle can later pick up the rider. The second self-driving vehicle can drop off the rider, and then the first self-driving vehicle can later pick up the rider.

In some embodiments, a vehicle management system comprises a computer system having at least one computer. The computer system can be configured to be communicatively coupled (e.g., one-time, intermittently, continuously) with a remote computing device of the rider. The computer system can be configured to be communicatively coupled (e.g., one-time, intermittently, continuously) with at least one of the first self-driving vehicle and the second self-driving vehicle.

In some embodiments, a vehicle management system comprises a location tracking system communicatively coupled (e.g., one-time, intermittently, continuously) with the computer system and configured to receive a first location data indicative of a drop-off location where the self-driving vehicle fleet dropped off the rider.

In some embodiments, the location tracking system is configured to receive a second location data indicative of at least one location of the remote computing device during at least a portion of a period from after when the self-driving vehicle fleet drops off the rider to before when the self-driving vehicle fleet picks up the rider.

In some embodiments, the computer system is configured to prompt at least one of the first self-driving vehicle and the second self-driving vehicle to drive to an area within 100 feet and/or within 250 feet of the drop-off location to pick up the rider in response to determining that the second location data is indicative of the remote computing device having returned to the area after being dropped off.

In some embodiments, the first location data comprises a first GPS location calculated by at least one of the first self-driving vehicle and the second self-driving vehicle. The second location data can comprise a second GPS location calculated by the remote computing device.

In some embodiments, the computer system is configured to prompt at least one of the first self-driving vehicle and the second self-driving vehicle to drive to the area in response to determining, based on the second location data, that the rider is not leaving the area (after having left the area and then having returned to the area).

In some embodiments, the first location data comprises a first GPS location based on at least a first radio signal and a second radio signal (and in some cases additional radio signals) received by at least one of the first self-driving vehicle and the second self-driving vehicle. In some embodiments, the second location data comprises a second GPS location based on at least a third radio signal and a fourth radio signal (and in some cases additional radio signals) received by the remote computing device.

In some embodiments, the first location data comprises a first GPS location calculated by at least one of the first self-driving vehicle and the second self-driving vehicle. The second location data can comprise a first indoor location calculated by the remote computing device based on information received via radio waves from an indoor positioning system.

In some embodiments, the computer system is configured to prompt at least one of the first self-driving vehicle and the second self-driving vehicle to drive to the area in response to determining, based on movement data from the remote computing device, that the rider is not leaving the area. The remote computing device can comprise at least one of an accelerometer, a gyroscope, and a Wi-Fi tracker. The movement data can be based on information from at least one of the accelerometer, the gyroscope, and the Wi-Fi tracker.

In some embodiments, the vehicle management system comprises program instructions configured to be executed by the remote computing device having at least one of an accelerometer, a gyroscope, and a Wi-Fi tracker. The program instructions can be configured to cause the remote computing device to send a first communication to the computer system in response to the remote computing device using at least one of the accelerometer, the gyroscope, and the Wi-Fi tracker to determine that the rider is not moving away from at least one of the area and the drop-off location. The computer system can be configured to prompt at least one of the first self-driving vehicle and the second self-driving vehicle to drive to the area in response to receiving the first communication.

In some embodiments, the program instructions are configured to cause the remote computing device to send a first communication to the computer system in response to the remote computing device using at least one of the accelerometer, the gyroscope, and the Wi-Fi tracker to determine that the rider is not moving in a manner indicative of leaving the area.

Some embodiments comprise program instructions configured to be executed by the remote computing device having at least one of an accelerometer and a gyroscope. The program instructions can be configured to cause the remote computing device to send a first communication to the computer system in response to the remote computing device using at least one of the accelerometer and the gyroscope to determine that the rider is not walking. The computer system can be configured to prompt at least one of the first self-driving vehicle and the second self-driving vehicle to drive to the area in response to receiving the first communication.

In some embodiments, the computer system comprises at least one processor and a memory having program instructions that when executed by the at least one processor cause the at least one processor to automatically prompt at least one of the first self-driving vehicle and the second self-driving vehicle to drive to an area within 100 feet and/or within 250 feet of the drop-off location to pick up the rider prior to a scheduled pick-up time in response to determining that the second location data is indicative of the remote computing device having returned to the area after being dropped off.

In some embodiments, the computer system comprises at least one processor and a memory having program instructions that when executed by the at least one processor are configured to cause the at least one processor to prompt the first self-driving vehicle to drive to an area within 100 feet and/or within 250 feet of the drop-off location to pick up the rider in response to determining that the second location data is indicative of the remote computing device having returned to the area after being dropped off.

In some embodiments, after prompting the first self-driving vehicle to drive to the area in response to determining that the second location data is indicative of the remote computing device having returned to the area, the program instructions are configured to cause the at least one processor to prompt the first self-driving vehicle to drive away from the area in response to determining that the second location data is indicative of the remote computing device moving away from at least one of the drop-off location and the area.

In some embodiments, the location tracking system is configured to receive the first location data indicative of the drop-off location where the first self-driving vehicle dropped off the rider. The computer system can be configured to prompt the second self-driving vehicle to drive to an area within 100 feet of the drop-off location to pick up the rider in response to determining that the second location data is indicative of the remote computing device having returned to the area after being dropped off.

In some embodiments, the computer system is configured to automatically prompt the first self-driving vehicle to drive to an area within 100 feet and/or within 250 feet of the drop-off location to pick up the rider in response to determining that the second location data is indicative of the remote computing device moving toward the area.

In some embodiments, the computer system is configured to automatically prompt the first self-driving vehicle to drive to an area within 100 feet and/or within 250 feet of the drop-off location to pick up the rider in response to determining that the second location data is indicative of the remote computing device moving toward the area for at least a predetermined amount of time.

In some embodiments, the computer system comprises at least one processor and a memory having program instructions that when executed by the at least one processor are configured to cause the at least one processor to cause the remote computing device to prompt the rider to at least one of request a ride, confirm the rider wants the ride, cancel a pending pick up, enter a pick-up time, and enter a pick-up location. The program instructions can be configured to cause the at least one processor to cause the remote computing device to prompt the rider in response to determining that the second location data is indicative of the remote computing device moving toward an area within 100 feet and/or within 250 feet of the drop-off location.

In some embodiments, at least one of the computer system and the remote computing device is configured to estimate a first amount of time that the first self-driving vehicle is away from a first area within 100 feet, within 900 feet, and/or within one mile of the drop-off location. At least one of the computer system and the remote computing device can be configured to estimate a second amount of time that the remote computing device is away from a second area within 100 feet, within 900 feet, and/or within 0.4 miles of the drop-off location. The computer system can be configured to automatically prompt the first self-driving vehicle to drive to a third area within 100 feet and/or within 250 feet of the drop-off location to pick up the rider in response to determining that the second location data is indicative of the remote computing device moving toward the area and in response to determining that the first amount of time is at least fifty percent of the second amount of time.

In some embodiments, the computer system comprises a memory having a third location data indicative of a pick-up location selected by the rider. The computer system can be configured to automatically prompt the first self-driving vehicle to drive to an area within 100 feet and/or within 250 feet of the pick-up location to pick up the rider in response to determining that the second location data is indicative of the remote computing device having arrived at the area.

In some embodiments, the memory comprises a pick-up time (e.g., data representing a time) selected by the rider. The computer system can be configured to override the pick-up time by prompting the self-driving vehicle to drive to the area to pick up the rider. The computer system can be configured to override the pick-up time in response to determining that the second location data is indicative of the remote computing device having arrived at the area prior to the pick-up time.

In some embodiments, the computer system comprises at least one processor and a memory having a pick-up time selected by the rider and having a third location data indicative of a pick-up location selected by the rider. The memory can comprise program instructions that when executed by the at least one processor are configured to cause the at least one processor to cause the remote computing device to prompt the rider to at least one of request a ride, confirm the rider wants the ride, and cancel a pending pick up. The program instructions can be configured to cause the at least one processor to cause the remote computing device to prompt the rider in response to determining that the second location data is indicative of the remote computing device having arrived at an area within 100 feet and/or within 250 feet of the pick-up location prior to the pick-up time.

In some embodiments, the computer system comprises a memory having a third location data indicative of a pick-up location selected by the rider. The computer system can be configured to automatically prompt the first self-driving vehicle to drive to an area within 100 feet and/or within 250 feet of the pick-up location to pick up the rider in response to determining that the second location data is indicative of the remote computing device moving toward the area.

Some embodiments comprise using a vehicle management system comprising a self-driving vehicle fleet having at least one of a first self-driving vehicle and a second self-driving vehicle. The fleet can be configured to transport a rider.

Some embodiments comprise receiving, by the vehicle management system, a first location data indicative of a drop-off location where the self-driving vehicle fleet dropped off the rider. Some embodiments comprise receiving, by the vehicle management system, a second location data indicative of at least one location of a remote computing device of the rider during at least a portion of a period from after when the self-driving vehicle fleet drops off the rider to before when the self-driving vehicle fleet picks up the rider.

Some embodiments comprise prompting, by the vehicle management system, the first self-driving vehicle to drive to an area within 100 feet and/or within 250 feet of the drop-off location to pick up the rider in response to determining that the second location data is indicative of the remote computing device having returned to the area after being dropped off.

In some embodiments, the first location data comprises a first GPS location calculated by at least one of the first self-driving vehicle and the second self-driving vehicle. The second location data can comprise a second GPS location calculated by the remote computing device. Some embodiments comprise prompting the first self-driving vehicle to drive to the area in response to determining, based on the second location data, that the remote computing device is not moving away from at least one of the area and the drop-off location.

In some embodiments, the remote computing device comprises at least one of an accelerometer and a gyroscope. Some embodiments comprise using at least one of the accelerometer and the gyroscope to collect movement data. Some embodiments comprise prompting the first self-driving vehicle to drive to the area in response to determining, based on the movement data, that the rider is not at least one of moving away from the area, moving away from the drop-off location, and moving more than a predetermined threshold.

In some embodiments, after prompting (e.g., by at least one of the vehicle management system, a computer system, a self-driving vehicle, and the remote computing device) the self-driving vehicle to drive to the area in response to determining that the second location data is indicative of the remote computing device having returned to the area, some embodiments comprise prompting (e.g., by the vehicle management system) the first self-driving vehicle to drive away from the area in response to determining (e.g., by at least one of the vehicle management system, a computer system, a self-driving vehicle, and the remote computing device) that the second location data is indicative of the remote computing device moving away from at least one

Some embodiments comprise prompting the first self-driving vehicle to drive to an area within 100 feet and/or within 250 feet of the drop-off location to pick up the rider in response to determining (e.g., by at least one of the vehicle management system, a computer system, a self-driving vehicle, and the remote computing device) that the second location data is indicative of the remote computing device moving toward the area.

Some embodiments comprise prompting, by the remote computing device, the rider to at least one of request a ride, confirm the rider wants the ride, cancel a pending pick up, enter a pick-up time, and enter a pick-up location, wherein the prompting is in response to determining (e.g., by at least one of the vehicle management system, the vehicle, and the remote computing device) that the second location data is indicative of the remote computing device moving toward an area within 100 feet and/or within 250 feet of the drop-off location.

Some embodiments comprise estimating (e.g., by at least one of the vehicle management system, a computer system, a self-driving vehicle, and the remote computing device) a first amount of time that the first self-driving vehicle is away from a first area within 100 feet, within 250 feet, within one mile, and/or within three miles of the drop-off location. Some embodiments comprise estimating (e.g., by at least one of the vehicle management system, a computer system, a self-driving vehicle, and the remote computing device) a second amount of time that the remote computing device is away from a second area within 100 feet and/or within 250 feet of the drop-off location.

Some embodiments comprise prompting (e.g., by at least one of the vehicle management system, the computer system, a self-driving vehicle, and the remote computing device) the first self-driving vehicle to drive to a third area within 100 feet and/or within 250 feet of the drop-off location to pick up the rider in response to determining that the second location data is indicative of the remote computing device moving toward the area and in response to determining that the first amount of time is at least fifty percent of the second amount of time.

In some embodiments, a memory of the vehicle management system comprises a pick-up time chosen by the rider and a third location data indicative of a pick-up location chosen by the rider. Some embodiments comprise prompting, by the remote computing device, the rider to at least one of request a ride, confirm the rider wants the ride, and cancel a pending pick up. The prompting can be in response to determining that the second location data is indicative of the remote computing device having arrived at a pick-up area within 100 feet and/or within 250 feet of the pick-up location prior to the pick-up time. The prompting can be in response to determining that the second location data is indicative of the remote computing device moving towards an area within 100 feet and/or within 250 feet of the pick-up location prior to the pick-up time. The prompting can be in response to determining that the second location data is indicative of the remote computing device moving towards an area (for at least a predetermined period of time) within 100 feet and/or within 250 feet of the pick-up location prior to the pick-up time.

In some embodiments, a memory of the vehicle management system comprises a pick-up time chosen by the rider. Some embodiments comprise overriding the pick-up time previously chosen by the rider in response to determining (e.g., by the vehicle management system) that the second location data is indicative of the remote computing device having returned to the area. Some embodiments comprise overriding the pick-up time previously chosen by the rider in response to determining (e.g., by the vehicle management system) that the second location data is indicative of the remote computing device moving towards the area.

In some embodiments, a memory of the vehicle management system comprises a third location data indicative of a pick-up location chosen by the rider. Some embodiments comprise prompting (e.g., by the vehicle management system) the first self-driving vehicle to drive to an area within 100 feet and/or within 250 feet of the pick-up location to pick up the rider in response to determining (e.g., by the vehicle management system) that the second location data is indicative of the remote computing device (and thus the rider) having arrived at the area. Some embodiments comprise prompting (e.g., by the vehicle management system) the first self-driving vehicle to drive to an area within 100 feet and/or within 250 feet of the pick-up location to pick up the rider in response to determining (e.g., by the vehicle management system) that the second location data is indicative of the remote computing device moving toward the area.

In some embodiments, the memory comprises a pick-up time selected by the rider. Some embodiments comprise overriding the pick-up time and prompting (e.g., by the vehicle management system) the first self-driving vehicle to drive to the area to pick up the rider prior to the pick-up time. The overriding and the prompting can be in response to determining that the second location data is indicative of the remote computing device (and thus the rider) having arrived at the area prior to the pick-up time.

In some embodiments, the area comprises a first area that includes the drop-off location. In some embodiments, the area comprises a second area that includes a pick-up location selected by the rider but does not include the drop-off location.

In some embodiments, a memory of the vehicle management system comprises a third location data indicative of a pick-up location chosen by the rider. Some embodiments comprise prompting (e.g., by the vehicle management system) the first self-driving vehicle to drive to an area within 100 feet and/or within 250 feet of the pick-up location to pick up the rider in response to determining that the second location data is indicative of the remote computing device moving toward the area.

In some embodiments, the computer system comprises at least one processor and a memory having program instructions that when executed by the at least one processor are configured to estimate a first amount of time that the first self-driving vehicle is away from a first area within feet and/or within two miles of the drop-off location, and estimate a second amount of time that the remote computing device is away from a second area within 100 feet and/or within 500 feet of the drop-off location. The computer system can be configured to automatically prompt the first self-driving vehicle to drive to a third area within 100 feet and/or within 250 feet of the drop-off location to pick up the rider in response to determining that the second location data is indicative of the rider moving toward the area and/or in response to determining that the second amount of time is within plus or minus fifty percent of the first amount of time.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages are described below with reference to the drawings, which are intended to illustrate, but not to limit, the invention. In the drawings, like reference characters denote corresponding features consistently throughout similar embodiments.

FIG. 1 illustrates a perspective view of a self-driving vehicle, according to some embodiments.

FIG. 2 illustrates a perspective view of a top side, a front side and a passenger side of a detection system, according to some embodiments.

FIG. 3 illustrates a perspective view of the top side, a backside side and a driver side of the detection system, according to some embodiments.

FIG. 4 illustrates a diagrammatic view of portions of a self-driving vehicle, according to some embodiments.

FIG. 5 illustrates a diagrammatic view of portions of a system, according to some embodiments.

FIG. 6 illustrates a diagrammatic view of a remote computing device, according to some embodiments.

FIG. 7 illustrates a diagrammatic view of a first self-driving vehicle and a second self-driving vehicle moving toward a drop-off location, according to some embodiments.

FIG. 8 illustrates a diagrammatic view illustrating a time after a rider was dropped off at the drop-off location, according to some embodiments.

FIG. 9 illustrates a diagrammatic view illustrating another time after a rider was dropped off at the drop-off location, according to some embodiments.

FIG. 10 illustrates a route taken by a rider from when she was dropped off at the drop-off location, according to some embodiments.

FIG. 11 illustrates a diagrammatic view of the remote computing device having returned to the drop-off area after having left the drop-off area, according to some embodiments.

DETAILED DESCRIPTION

Although certain embodiments and examples are disclosed below, inventive subject matter extends beyond the specifically disclosed embodiments to other alternative embodiments and/or uses, and to modifications and equivalents thereof. Thus, the scope of the claims appended hereto is not limited by any of the particular embodiments described below. For example, in any method or process disclosed herein, the acts or operations of the method or process may be performed in any suitable sequence and are not necessarily limited to any particular disclosed sequence. Various operations may be described as multiple discrete operations in turn, in a manner that may be helpful in understanding certain embodiments; however, the order of description should not be construed to imply that these operations are order dependent. Additionally, the structures, systems, and/or devices described herein may be embodied as integrated components or as separate components.

For purposes of comparing various embodiments, certain aspects and advantages of these embodiments are described. Not necessarily all such aspects or advantages are achieved by any particular embodiment. Thus, for example, various embodiments may be carried out in a manner that achieves or optimizes one advantage or group of advantages as taught herein without necessarily achieving other aspects or advantages as may also be taught or suggested herein.

Self-driving vehicles will save tens of thousands of lives per year. The majority of vehicle-related deaths are caused by driver errors. Tests have shown that self-driving vehicles nearly eliminate self-inflicted accidents (although they are not immune to accidents caused by human drivers of other vehicles).

Self-driving vehicles typically have unlimited attention spans and can process complex sensor data nearly instantaneously. (Alphabet Inc. and Tesla Motors Inc. have built self-driving vehicles.) The ability of self-driving vehicles to save lives is so impressive that society has a moral imperative to develop self-driving technology such that it can be widely adopted.

Although self-driving vehicles will unlock many safety benefits, there are several barriers to rapid adoption of self-driving vehicles. Some of the embodiments described herein overcome several of these barriers.

Self-driving vehicles are sometimes referred to as autonomous cars, autonomous vehicles, driverless cars, and driverless vehicles. Various levels of “self-driving” behaviors are available to sense surrounding environments and navigate appropriately (e.g., without hitting objects, in a time-efficient manner). Levels of self-driving vehicles comprise Level 1 (Driver Assistance), Level 2 (Partial Automation), Level 3 (Conditional Automation), Level 4 (High Automation), and Level 5 (Full Automation). Of course, other levels and distinctions are possible. The National Highway Traffic Safety Administration has outlined various levels of self-driving vehicle automation based on information from the Society of Automotive Engineers.

Some embodiments can be used with self-driving vehicles. Embodiments, however, are not limited to self-driving vehicles and can be used with non-self-driving vehicles.

As used herein, “location” is used broadly and is not limited to a street address. A location can be a Global Positioning System (“GPS”) location and can be any other location indicator. A location can be an outdoor location. A location can be an indoor location (e.g., a location inside a large shopping center, an apartment complex or other building).

Some embodiments use iBeacon hardware to enable tracking remote computing devices indoors. iBeacon is a protocol developed by Apple Inc. Several embodiments use radio transceivers (such as Bluetooth transceivers) to enable tracking remote computing devices indoors.

Some embodiments use Global Positioning System (“GPS”) hardware to determine an outdoor location of a remote computing device. GPS can include the system of satellites put into orbit and maintained by the U.S. Department of Defense, Russia's GLONASS satellite system, assisted GPS systems, and/or any satellite system used to provide location data.

In some embodiments, each system comprises at least one processor and a memory comprising program instructions that when executed by the at least one processor cause the system to perform any of the method steps described herein and/or incorporated by reference.

FIG. 1 illustrates a perspective view of a self-driving vehicle 5. The self-driving vehicle 5 can include a detection system 7 configured to detect objects (e.g., cars, pedestrians, other vehicles, buildings, fire hydrants, trees, lane markers, guard rails, roadway barriers, sidewalks, roadway signs, traffic lights) located around the self-driving vehicle 5. Various sensors of the detection system 7 can sense objects even closer than an inch away (e.g., by using ultrasonic sensors) and even farther away than 100 yards (e.g., using long-range radar).

FIG. 2 illustrates a perspective view of the top side, the front side and the passenger side of the detection system 7. FIG. 3 illustrates a perspective view of the top side, the backside side and the driver side of the detection system 7. FIG. 4 illustrates a diagrammatic view of portions of a self-driving vehicle 5, according to some embodiments.

The detection system 7 can comprise radar 8, lidar 9, ultrasonic sensors, cameras 11, and any other sensing devices configured to enable the vehicle 5 to detect objects.

The self-driving vehicle 5 illustrated in FIGS. 1-4 includes a detection system 7 mounted to the roof of the self-driving vehicle 5. In some embodiments, however, the components of the detection system 7 are mounted on different areas of the self-driving vehicle 5. For example, the ultrasonic sensors can be mounted on the bumpers of the self-driving vehicle 5. The short range of the ultrasonic sensors can make bumper mounting helpful (because the bumper is often closer to the objects being sensed). The cameras 11 can be mounted just behind the windshield (e.g., in the rearview mirror) and just behind other windows. The radars 8 can be mounted near each of the four corners of the self-driving vehicle 5. In the illustrated embodiment, however, the detection system 7 can be contained in one assembly to simplify the integration of the detection system 7 into a vehicle.

The detection system 7 can use cameras 11 mounted around a perimeter (e.g., around a perimeter of the vehicle 5 or around a perimeter of a housing of the detection system 7). As illustrated in FIGS. 1-4, the cameras 11 face forward, backward, left, and right to provide (collectively) a 360 degree view around the vehicle 5. The cameras 11 can be high-resolution cameras covered by a glass window to protect each cameras 11 from water and dirt.

Cameras 11 can be configured to see lane markers on a road. Using cameras 11 to see painted lane markers can be helpful (because painted lane markers sometimes lack enough three dimensional nature to be detected by some other sensors). In addition, cameras 11 can see color differences (e.g., the difference between the color of the asphalt and the color of yellow or white paint of a lane marker). Cameras 11 can see the color of traffic lights (e.g., red, yellow, green).

Cameras 11 sometimes have trouble seeing in situations where the human eye would have trouble seeing (e.g., in fog or rain).

Radars 8 can be very helpful in fog and rain. An object that is not detected by cameras 11 (e.g., due to fog or rain) can be detected by radar 8. Radars 8 can detect the speed of other vehicles and the distance to other vehicles. Radars 8 can also detect objects that are far away.

Radar is an object-detection system that uses radio waves to determine the range, angle, or velocity of objects. A radar can comprise a transmitter producing electromagnetic waves in the radio or microwave domain, a transmitting antenna, a receiving antenna (which can be the same antenna as the transmitting antenna), a receiver, and/or a processor to determine properties of the objects detected by the radar.

Lidar uses light to detect objects. A lidar 9 can be located on the top portion of the detection system 7 to provide a 360 degree view of the area around the self-driving vehicle 5. The lidar 9 can tell the difference between an actual person and a billboard that includes a picture of a person (due to the three dimensional nature of the actual person and the two dimensional nature of the picture of a person).

The lidar 9 can accurately sense the three dimensional nature of the world around the self-driving vehicle 5. The lidar 9 can also measure the distance to objects. Measuring distance can enable the self-driving vehicle 5 to know, for example, if an approaching car is 5 meters away (so there is not enough time to turn in front of the car) or 25 meters away (so there may be enough time to turn in front of the car).

In some embodiments, the lidar 9 is a Velodyne VLS-128 made by Velodyne LiDAR, Inc. having an office in San Jose, Calif. The Velodyne VLS-128 can provide real-time, three-dimensional data with up to 0.1 degree vertical and horizontal resolution, a range of up to 300 meters, and a 360-degree surround view. The VLS-128 can provide the range, resolution and accuracy required by some of the most advanced autonomous vehicle programs in the world.

Many types of lidars can be used. Some embodiments use “incoherent” or direct energy detection (which principally measures amplitude changes of the reflected light). Some embodiments use coherent detection (which in some cases can be well suited for measuring Doppler shifts, or changes in phase of the reflected light). Coherent systems can use optical heterodyne detection.

Lidar can use pulse models. Some lidar embodiments use micropulse or high energy. Micropulse systems can use intermittent bursts of energy. Some lidar embodiments use high-power systems.

Lidar can comprise lasers. Some embodiments include solid-state lasers. Some embodiments include flash lidar. Some embodiments include electromechanical lidar. Some embodiments include phased arrays to illuminate any direction by using a microscopic array of individual antennas. Some embodiments include mirrors (e.g., micro electromechanical mirrors). Some embodiments include dual oscillating plane mirrors, a polygon mirror and/or a scanner (e.g., a dual-axis scanner).

Lidar embodiments can include photodetector and receiver electronics. Any suitable type of photodetector can be used. Some embodiments include solid-state photodetectors (e.g., silicon avalanche photodiodes) and/or photomultipliers.

The motion of the vehicle 5 can be compensated for to accurately determine the location, speed, and direction of objects (such as other vehicles) located outside the vehicle 5. For example, if a first vehicle 5 a is heading west at 35 miles per hour and a second vehicle is heading east at an unknown speed, a detection system 7 a of the first vehicle 5 a can remove the contribution of the 35 miles per hour when determining the speed of the second vehicle.

In some embodiments, motion of the vehicle 5 is compensated for by using position and navigation systems to determine the absolute position, speed, and orientation of the lidar, camera, radar, or other object sensing system. A Global Positioning System (“GPS”) receiver and/or an Inertial Measurement Unit (“IMU”) can be used to determine the absolute position and orientation of the object sensing system.

Lidar can use active sensors that supply their own illumination source. The energy can hit objects. The reflected energy can be detected and measured by sensors. Distance to the object can be determined by recording the time between transmitted and backscattered pulses and by using the speed of light to calculate the distance traveled. Scanning can be used to create a three dimensional image or map of the area around the vehicle 5.

Embodiments can use a short-range lidar to give the self-driving vehicle 5 a surround view near the self-driving vehicle 5 (to see objects close to the self-driving vehicle 5) and can use a long-range lidar configured to not only detect objects located far from the self-driving vehicle 5, but also to enable zooming into objects that are over 200 meters away. The long-range lidar can be very helpful at high-speed highway situations.

Lidar uses light to detect a distance to an object, a direction to the object, and/or a location of an object. Lidar can use pulsed laser light emitted by a laser.

The light can reflect off objects around the vehicle. These reflections can be detected by a sensor of the lidar. Measuring how long the light takes to return to the sensor and measuring the wavelengths of the reflected light can enable making a three-dimensional model of the object being sensed and of the entire area around the vehicle 5.

FIG. 4 illustrates a diagrammatic view of portions of a self-driving vehicle 5, according to some embodiments. The self-driving vehicle 5 can include a vehicle navigation system 14, a communication system 16 that has a transmitter 18 and a receiver 17, a computer system 19 that has a processor 26, a memory 20 that has program instructions 27 and map information 28, a traffic monitor 23, and a drive-by-wire system 24. In some embodiments, at least some of these items are part of the detection system 7.

The vehicle navigation system 14 can be configured to enable the vehicle 5 to follow a driving route. The vehicle navigation system 14 can direct the vehicle toward a pick-up location.

The communication system 16 can be configured to communicate with a vehicle management system. The communication system 16 can be configured to communicate with a remote computing device of a rider. The communication system 16 can use an antenna 13 to communicate with other vehicles and other devices (such as a vehicle management system and remote computing devices) via intermediary communication systems 15.

Intermediary communication systems 15 can comprise wireless networks, Wi-Fi routers, Bluetooth systems, cellular networks, telephone networks, Internet systems, servers, cloud computing, remotely located computers, satellite systems, communication systems, and any other suitable means of enabling communication between the various components of embodiments described herein and/or incorporated by reference.

The drive-by-wire system 24 can be a computer-regulated system for controlling the engine, accelerating, braking, steering, signaling, handling, suspension, and/or other functions related to autonomously driving the vehicle 5.

In some embodiments, at least portions of a vehicle management system are located far away from vehicles 5, 5 a, 5 b, 5 c. The vehicle management system can include software that is run on servers. The servers can communicate with vehicles 5, 5 a, 5 b, 5 c via intermediary communication systems 15.

In some embodiments, portions of the vehicle management system are located in one or more vehicles 5, 5 a, 5 b, 5 c and portions of the vehicle management system are located far away from the one or more vehicles 5, 5 a, 5 b, 5 c.

FIG. 5 illustrates a diagrammatic view of portions of a vehicle management system, according to some embodiments. FIG. 5 illustrates many optional items. Not all the items illustrated in FIG. 5 are necessarily part of each vehicle management system.

A vehicle management system can comprise a location tracking system 30 configured to track locations of vehicles 5, 5 a, 5 b, 5 c and also configured to track locations of vehicles that have been identified as potentially impaired (according to indications collected by the vehicles 5, 5 a, 5 b, 5 c).

The location tracking system 30 can receive GPS location data of the vehicles 5, 5 a, 5 b, 5 c by the vehicles 5, 5 a, 5 b, 5 c sending their GPS location data to the location tracking system 30 via intermediary communication systems 15.

The vehicles 5, 5 a, 5 b, 5 c can received radio communicates from GPS satellites. These radio communications can include information configured to enable the vehicles 5, 5 a, 5 b, 5 c to calculate their position at any time.

Receiving radio communications (with position data) from three or more GPS satellites can provide data to enable each vehicle and each remote computing device to calculate its own position. Then each vehicle and each remote computing device can send its position data to a vehicle management system (e.g., via intermediary communication systems 15).

Each device can receive radio signals broadcasted from GPS satellites. Then, the device can calculate how far it is away from the broadcasting satellite by determining how long the radio signal (traveling at lightspeed) took to arrive at the device. Trilateration (based on data from at least three GPS satellites) enables the device to know where it is located. The device can then send its location to the vehicle management system. A location tracking system can receive the location data from the vehicle management system, from the device, and/or from any other system.

The location tracking system 30 can comprise a computer configured to receive locations of vehicles and remote computing devices. The location tracking system 30 can comprise a processor 35 and a memory 31 comprising program instructions 32 configured such that when executed by the processor 35 the program instructions 32 cause the location tracking system 30 to monitor locations of vehicles and remote computing devices.

A vehicle management system can comprise a computer system 34 that includes one or more computers of any suitable type. Each computer can include a processor 35 and a memory 31 comprising program instructions 32 configured such that when executed by the processor 35 the program instructions 32 cause the vehicle management system to perform the methods described herein. The computer system 34 can comprise a database 33 having information.

The vehicle management system can comprise map information 37 (including street information, preferred pick-up locations, and preferred drop-off locations) and a traffic monitor 38 configured to receive traffic information from third parties (e.g., Google Maps).

The vehicle management system can comprise a communication system 39 having a transmitter 40, a receiver 41, and an antenna 42. The communication system 39 can be configured to communicate with the vehicles 5, 5 a, 5 b, 5 c. In some embodiments, the communication system 39 communicates with the vehicles 5, 5 a, 5 b, 5 c via intermediary communication systems 15. The antenna 42 can be communicatively coupled to the antenna 13 shown in FIG. 4.

The antenna 42 can be communicatively coupled (e.g., via intermediary communication systems 15) with self-driving vehicles 5, 5 a, 5 b, 5 c that can include a vehicle navigation system 14, a communication system 16 that has a transmitter 18 and a receiver 17, a computer system 19 that has a processor 26, a memory 20 that has program instructions 27 and map information 28, a traffic monitor 23, and a drive-by-wire system 24 (as illustrated in FIG. 4).

Communicative coupling may be via continuous communications or intermittent communications. Intermittent communications can be via periodic communications (e.g., every 1 second, every 60 seconds, every 10 minutes). As used herein, “periodically” does not imply that every period has the same duration. In some embodiments, the communicative coupling is via intermediary communication systems 15.

Each self-driving vehicle 5 a, 5 b, 5 c can include all of the items described in the context of vehicle 5.

Vehicle 5 a includes a detection system 7 a that can include all of the items described in the context of detection system 7. Vehicle 5 b includes a detection system 7 b that can include all of the items described in the context of detection system 7. Vehicle 5 c includes a detection system 7 c that can include all of the items described in the context of detection system 7.

FIG. 6 illustrates a diagrammatic view of a remote computing device 12. Many different types of remote computing devices can be used with the embodiments described herein and/or incorporated by reference. Some remote computing devices do not include all the parts illustrated in FIG. 6. Some remote computing devices include parts not illustrated in FIG. 6.

A remote computing device can be a smartphone, a tablet computer, a laptop computer, a desktop computer, a server, augmented reality glasses, an implanted computer, and/or any type of computer. A rider can bring her remote computing device into the self-driving vehicle, use her remote computing device in the self-driving vehicle, and leave the self-driving vehicle with her remote computing device. In some embodiments, the rider requests a ride at her home with a remote computing device, but then leaves the remote computing device at home when she goes to get a ride from the self-driving vehicle.

The remote computing device 12 can comprise an accelerometer 74, a barometer 75 (which can include an altimeter), a gyroscope 76, a WiFi tracker 77, a compass 78, a location tracking system 79, a memory 80, a computer system 82 having a processor 83, a database 84 and/or a communication system 86. The communication system can include a transmitter 87, a receiver 88, and/or an antenna 89. The remote computing device 12 can comprise a display screen 90 configured to display images to a rider. The remote computing device 12 can comprise a speaker configured to emit sounds to the rider. The remote computing device 12 can comprise a microphone configured to record sounds from the rider.

Self-driving vehicles can include cars, vans, trucks, buses, scooters, motorcycles, helicopters, quadcopters, flying machines, air taxis, planes, and any motorized vehicle configured to transport a person.

A person (i.e., a rider) can enter (and/or ride on) a first self-driving vehicle 5 a or a second self-driving vehicle 5 b. Whichever vehicle the rider enters can transport the rider to a drop-off location 50.

FIG. 7 illustrates a diagrammatic view of the first self-driving vehicle 5 a and the second self-driving vehicle 5 b moving toward a drop-off location 50 (as indicated by arrows 93, 94). The rider can be located in either the first self-driving vehicle 5 a or the second self-driving vehicle 5 b.

In some cases, the first self-driving vehicle 5 a transports the rider to the drop-off location 50 and the second self-driving vehicle 5 b picks up the rider. A self-driving vehicle fleet can comprise one vehicle, two vehicles, three vehicles or more vehicles. In some embodiments, a self-driving vehicle fleet comprises hundreds or even thousands of vehicles. The vehicles can coordinate to provide efficient transportation to the rider (and/or to many riders).

The first self-driving vehicle 5 a can receive communications (e.g., radio signals) from positioning systems 53 (which in some embodiments are GPS satellites). The second self-driving vehicle 5 b can receive communications (e.g., radio signals) from positioning systems 53 (which in some embodiments are GPS satellites).

Positioning systems 53 (e.g., GPS satellites) can send communications 57 a, 57 b, 57 c (e.g., radio signals) to the first self-driving vehicle 5 a. The first self-driving vehicle 5 a can use these communications 57 a, 57 b, 57 c to determine positions of the first self-driving vehicle 5 a at various times (e.g., when the first self-driving vehicle 5 a drops off the rider at the drop-off location 50).

The first self-driving vehicle 5 a can send communications 59 (which can include GPS coordinates of the first self-driving vehicle 5 a) to an antenna 42 via intermediary communication systems 15 a. Intermediary communication systems 15 a can send communications 60 (which can include GPS coordinates of the first self-driving vehicle 5 a) to the antenna 42. (Intermediary communication systems 15 a, 15 b, 15 c can include all of the features and systems described in the context of intermediary communication systems 15.)

Positioning systems 53 (e.g., GPS satellites) can send communications 57 e (e.g., radio signals) to the second self-driving vehicle 5 b. The second self-driving vehicle 5 b can use these communications 57 e to determine positions of the second self-driving vehicle 5 b at various times.

Receiving radio communications (with position data) from three or more GPS satellites can provide data to enable each vehicle 5 a, 5 b, 5 c and each remote computing device 12 (shown in FIG. 6) to calculate its own position. Then each vehicle 5 a, 5 b, 5 c and each remote computing device 12 can send its position data to a vehicle management system (e.g., via intermediary communication systems 15 a, 15 c).

The location tracking system 30 can receive GPS location data of the vehicles 5, 5 a, 5 b, 5 c by the vehicles 5, 5 a, 5 b, 5 c sending their GPS location data to the location tracking system 30 via intermediary communication systems 15 a, 15 c.

The vehicles 5, 5 a, 5 b, 5 c can received radio communicates from GPS satellites. These radio communications can include information configured to enable the vehicles 5, 5 a, 5 b, 5 c to calculate their position at any time.

Each device can receive radio signals broadcasted from GPS satellites. Then, the device can calculate how far it is away from the broadcasting satellite by determining how long the radio signal (traveling at lightspeed) took to arrive at the device. Trilateration (based on data from at least three GPS satellites) enables the device to know where it is located. The device can then send its location to the vehicle management system. A location tracking system 30 can receive the location data from the vehicle management system, from the device, and/or from any other system.

The location tracking system 30 can comprise a computer configured to receive locations of vehicles and remote computing devices. The location tracking system 30 can comprise a processor 35 and a memory 31 comprising program instructions 32 configured such that (when executed by the processor 35) the program instructions 32 cause the location tracking system 30 to monitor locations of vehicles 5, 5 a, 5 b, 5 c and remote computing devices 12.

In some cases, a rider uses a remote computing device 12 to request a ride from the vehicle management system. Then, the vehicle management system prompts a self-driving vehicle 5 to drive to a pick-up location selected by the rider (e.g., via an “app” on the remote computing device 12). Then, the rider must wait for the self-driving vehicle 5. This approach is inconvenient for the rider for many reasons including the rider having to perform several steps to use a remote computing device 12 to request a ride and the rider having to wait for the self-driving vehicle 5 to arrive. (The self-driving vehicle 5 could be many minutes away.) Thus, there is a need for systems and methods that eliminate the need for the rider to perform several steps using a remote computing device “app” to request a ride and/or that reduce the amount of time the rider has to wait for a ride.

Some embodiments predict whether the rider needs a ride, predict when the rider needs a ride and/or predict where the rider needs a ride using location data. Thus, some embodiments described herein and/or incorporated by reference dramatically increase the convenience of using a self-driving vehicle fleet for transportation.

If a rider has to perform several steps using a remote computing device “app” to request a ride and has to wait a long time for a ride, the rider may decide to buy a car and drive herself (rather than using a self-driving car fleet for transportation). Non-self-driving vehicles are far less safe than self-driving vehicles (to both the rider and to other people on the road), so the embodiments described herein and/or incorporated by reference are extremely important to expedite the adoption of self-driving vehicles.

The first self-driving vehicle 5 a can send communications 59 (which can include GPS coordinates of the first self-driving vehicle 5 a) to an antenna 42 via intermediary communication systems 15 a. Intermediary communication systems 15 a can send communications 60 (which can include GPS coordinates of the first self-driving vehicle 5 a) to the antenna 42.

The second self-driving vehicle 5 b can send communications 66 (which can include GPS coordinates of the second self-driving vehicle 5 b) to an antenna 42 via intermediary communication systems 15 c. Intermediary communication systems 15 c can send communications 63 (which can include GPS coordinates of the second self-driving vehicle 5 b) to the antenna 42.

In some embodiments, a vehicle management system comprises a self-driving vehicle fleet having at least one of a first self-driving vehicle 5 a and a second self-driving vehicle 5 b. The self-driving vehicle fleet can be configured to transport a rider.

In some embodiments, a vehicle management system comprises a computer system 34 having at least one computer. The computer system 34 can be configured to be communicatively coupled (e.g., one-time, intermittently, continuously) with a remote computing device 12 of the rider. The computer system 34 can be configured to be communicatively coupled (e.g., one-time, intermittently, continuously) with at least one of the first self-driving vehicle 5 a and the second self-driving vehicle 5 b.

In some embodiments, a vehicle management system comprises a location tracking system communicatively coupled (e.g., one-time, intermittently, continuously) with the computer system 34 and configured to receive a first location data indicative of a drop-off location 50 where the self-driving vehicle fleet dropped off the rider.

The first location data can include many different types of location data. Location data can include a GPS location, a street address, a location described by any type of positioning system, and/or any other data that is configured to indicate a location. In some embodiments, a GPS location is indicated by two numbers such as 47.606286, −122.341911. Location data can comprise other types of location indicators.

Some embodiments use Assisted GPS, which is a type of GPS. Assisted GPS can draw information from local cell towers and can enhance the performance of standard GPS.

FIG. 8 is a diagrammatic view illustrating a time after the rider was dropped off at the drop-off location 50. The rider walked along a route 95 from the drop-off location 50 to a final location 51 u near a pick-up location 69 and within a pick-up area 64. A second location data can comprise one or more locations 51 q, 51 r, 51 s, 51 t along the walking route 95. The locations 51 q, 51 r, 51 s, 51 t can be calculated by the remote computing device 12 based on communications 57 d, 58 d received (by the remote computing device 12) from positioning systems 53, 54. Some positioning systems 53 include GPS satellites. Some positioning systems 54 include indoor positioning systems.

Indoor positioning systems can enable the remote computing device 12 (and/or the vehicle management system) to determine a location of the remote computing device 12 inside a building (e.g., using radio waves, lights, magnetic fields, acoustic signals, or other sensory information sent from the indoor positioning systems). Determining a distance from the remote computing device 12 to anchor nodes (e.g., nodes with known positions) can enable the system to determine a precise location of the remote computing device 12. The method can use the same principles as are used in GPS location determinations (e.g., trilateration). Nodes can be WiFi access points, LiFi access points, Bluetooth beacons, and any other nodes configured to enable a remote computing device 12 to calculate the position of the remote computing device 12.

Some indoor positioning systems use iBeacon. iBeacon was developed by Apple Inc. iBeacon can use Bluetooth Low Energy (BLE) devices that broadcast their identifier (and other information) to nearby remote computing devices 12.

Some positioning systems use telecommunication systems to determine a position of a remote computing device 12 and/or to determine a location of a self-driving vehicle 5 a, 5 b, 5 c. In some embodiments, positioning systems using telecommunication systems use Long-Term Evolution (“LTE”) protocols. LTE is one of several standards for high-speed wireless communication for mobile devices and data terminals. LTE can be based on GSM/EDGE and UMTS/HSPA technologies.

Some positioning systems that use telecommunication systems are based on the LTE Positioning Protocol (“LPP”). Some positioning systems that use telecommunication systems are based on the LTE Positioning Protocol Annex (“LPPa”).

Some embodiments use Observed Time Difference Of Arrival (“OTDOA”). OTDOA is a positioning feature. OTDOA was introduced in rel9 E-UTRA (LTE radio). In OTDOA, the remote computing device 12 measures the time difference between signals from several E-UTRAN Node Bs. Then, the remote computing device 12 reports these time differences to a specific device in the network (e.g., the Evolved Serving Mobile Location Center, which can be abbreviated as “ESMLC”). Based on these time differences and knowledge of the E-UTRAN Node B locations, the device (e.g., the ESMLC) can calculate the position of the remote computing device 12.

WiFi trackers 77 can analyze WiFi signals (e.g., from several WiFi emitters) to determine if a remote computing device 12 is moving. As the remote computing device 12 moves toward a first WiFi source, the signal will get stronger. As the remote computing device 12 moves away from a second WiFi source, the signal will get weaker. This signal analysis can enable the WiFi tracker 77 to determine if the remote computing device 12 is moving.

In some embodiments, the location tracking system 30 is configured to receive a second location data indicative of at least one location 51 q, 51 r, 51 s, 51 t of the remote computing device 12 during at least a portion of a period from after when the self-driving vehicle fleet drops off the rider to before when the self-driving vehicle fleet picks up the rider. The at least one location 51 q, 51 r, 51 s, 51 t can comprise indoor locations and/or outdoor locations. The route 95 walked by the rider can move through a building (where indoor locations can be helpful) and can move through outdoor locations (e.g., through a parking lot, through a nature preserve, and/or along a street).

In some embodiments, the pick-up area 64 does not comprise the drop-off location but does comprise a pick-up location 69. Entering the pick-up area 64 can cause the first self-driving vehicle 5 a or the second self-driving vehicle 5 b to drive to the pick-up area 64 (e.g., as indicated by arrow 91) to pick-up the rider.

In some embodiments, the computer system 34 comprises a memory 31 having a third location data indicative of a pick-up location 69 selected by the rider. The computer system 34 can be configured to automatically prompt the first self-driving vehicle 5 a to drive to an area 64 within feet and/or within 250 feet of the pick-up location 69 to pick up the rider in response to determining that the second location data is indicative of the remote computing device 12 (and thus the rider) having arrived at the area 64.

In some embodiments, the memory 31 comprises a pick-up time (e.g., data representing a time of day) selected by the rider. The computer system 34 can be configured to override the pick-up time by (automatically) prompting the first self-driving vehicle 5 a to drive to the area 64 to pick up the rider. The computer system 34 can be configured to override the pick-up time in response to determining that the second location data is indicative of the remote computing device 12 having arrived at the area 64 prior to the pick-up time.

In some embodiments, the computer system 34 comprises at least one processor 35 and a memory 31 having a pick-up time selected by the rider and having a third location data indicative of a pick-up location 69 selected by the rider. The memory 31 can comprise program instructions 32 that when executed by the at least one processor 35 are configured to cause the at least one processor 35 to cause the remote computing device 12 to prompt the rider to at least one of request a ride, confirm the rider wants the ride, and cancel a pending pick up.

The remote computing device 12 can display text such as, “Would you like a ride now?” on the display screen 90 to prompt the rider to request a ride. (Of course, many other ways of prompting the rider to request a ride are within the scope of the various embodiments.) The rider can then reply, “Yes” (to receive a ride). The rider can also reply that she would like a ride at a time in the future at the pick-up location 69 or at another pick-up location.

The remote computing device 12 can prompt the rider to confirm that the rider wants a ride in many ways including displaying an icon on the display screen 90. The icon can include text that says, “Click here to confirm that you want a ride.”

In some embodiments, the vehicle management system automatically causes the vehicle 5 a to drive to the area 64 in response to determining that the remote computing device 12 is located inside the area 64 and/or schedules a pick-up in response to determining that the remote computing device 12 is located inside the area 64. These pending pick ups can be canceled by the rider via the remote computing device 12. In some embodiments, the display screen 90 includes text that says, “A car is on the way and will arrive in five minutes. To cancel this pick up, click here.”

The program instructions 32 can be configured to cause the at least one processor 35 to cause the remote computing device 12 to prompt the rider in response to determining that the second location data is indicative of the remote computing device 12 (and thus the rider) having arrived at an area 64 within 100 feet and/or within 250 feet of the pick-up location prior to the pick-up time.

In some embodiments, the vehicle management system determines (based on location data of locations 51 q, 51 r, 51 s, 51 t along the path 95) that the remote computing device 12 is moving toward at least one of the pick-up location 69 and the pick-up area 64.

In some embodiments, the computer system 34 comprises a memory 31 having a third location data (e.g., GPS coordinates, a street address) indicative of a pick-up location 69 selected by the rider. The computer system 34 can be configured to automatically prompt the first self-driving vehicle 5 a to drive to an area 64 within 100 feet and/or within 250 feet of the pick-up location 69 to pick up the rider in response to determining that the second location data (e.g., of the locations 51 q, 51 r, 51 s, 51 t) is indicative of the remote computing device 12 moving toward at least one of the pick-up location 69 and the pick-up area 64.

In some embodiments, a memory 31 of the vehicle management system comprises a pick-up time (e.g., data that indicates a time of day such as 4:21 PM) chosen by the rider. The memory of the vehicle management system can comprise a third location data indicative of a pick-up location 69 chosen by the rider. Some embodiments comprise prompting, by the remote computing device 12, the rider to at least one of request a ride, confirm the rider wants the ride, and cancel a pending pick up. The prompting can be in response to determining that the second location data is indicative of the remote computing device 12 having arrived at a pick-up area 64 within 100 feet and/or within 250 feet of the pick-up location 69 prior to the pick-up time. The prompting can be in response to determining that the second location data is indicative of the remote computing device 12 moving towards an area within 100 feet and/or within 250 feet of the pick-up location 69 prior to the pick-up time.

The prompting can be in response to determining that the second location data is indicative of the remote computing device 12 moving towards an area 64 (for at least a predetermined period of time) within 100 feet and/or within 250 feet of the pick-up location 69 prior to the pick-up time. In some embodiments, detecting (by the vehicle management system) that the remote computing device 12 has been moving toward the pick-up location 69 and/or area 64 for one minute will not cause the vehicle management system to prompt the first self-driving vehicle 5 a to drive to the area 64, but detecting (by the vehicle management system) that the remote computing device 12 has been moving toward the pick-up location 69 and/or area 64 for more than one minute and/or more than three minutes will cause the vehicle management system to prompt the first self-driving vehicle 5 a to drive to the area 64.

In some embodiments, a memory 31 of the vehicle management system comprises a third location data indicative of a pick-up location 69 chosen by the rider. Some embodiments comprise prompting (e.g., by the vehicle management system) the first self-driving vehicle 5 a to drive to an area 64 within 100 feet and/or within 250 feet of the pick-up location 69 to pick up the rider in response to determining (e.g., by the vehicle management system) that the second location data is indicative of the remote computing device 12 having arrived at the area 64 (e.g., as indicated by location 51 u).

Some embodiments comprise prompting (e.g., by the vehicle management system) the first self-driving vehicle 5 a to drive to an area 64 within 100 feet and/or within 250 feet of the pick-up location 69 to pick up the rider in response to determining (e.g., by the vehicle management system) that the second location data is indicative of the remote computing device 12 moving toward the area 64.

The rider can use the remote computing device 12 to select a pick-up location 69 and a pick-up time (such as 3:00 PM). The vehicle management system can prompt the first self-driving vehicle 5 a to be ready to pick up the rider at the area 64 at 3:00 PM. Sometimes, however, the rider may arrive at the area 64 prior to the scheduled pick-up time. For example, the rider might arrive at the area at 2:00 PM.

In some embodiments, the memory 31 comprises a pick-up time selected by the rider. Some embodiments comprise overriding the pick-up time and prompting (e.g., by the vehicle management system) the first self-driving vehicle 5 a to drive to the area 64 to pick up the rider prior to the pick-up time. The overriding and the prompting can be in response to determining that the second location data is indicative of the remote computing device 12 having arrived at the area 64 prior to the pick-up time.

In some embodiments, the area 55 (shown in FIG. 11) where the fleet picks up the rider comprises a first area that includes the drop-off location 50. In some embodiments, the area 64 (shown in FIG. 8) where the fleet picks up the rider comprises a second area that includes a pick-up location 69 selected by the rider but does not include the drop-off location 50.

In some embodiments, a memory 31 of the vehicle management system comprises a third location data indicative of a pick-up location 69 chosen by the rider. Some embodiments comprise prompting (e.g., by the vehicle management system and/or by the remote computing device 12) the first self-driving vehicle 5 a to drive to an area 64 within 100 feet and/or within 250 feet of the pick-up location 69 to pick up the rider in response to determining that the second location data is indicative of the remote computing device 12 moving toward the area 64. For example, if the remote computing device 12 moves from drop-off location 50 to locations 51 a, 51 r, 51 a, 51 t (but has not arrived at location 51 u) the system can determine that the remote computing device 12 is moving towards the area 64.

FIG. 7 illustrates a diagrammatic view of the first self-driving vehicle 5 a and the second self-driving vehicle 5 b moving toward a drop-off location 50 (as indicated by arrows 93, 94). The rider can be located in either the first self-driving vehicle 5 a or the second self-driving vehicle 5 b. The fleet can drop off the rider at the drop-off location 50. The rider can possess a remote computing device 12 (illustrated in FIGS. 6, 9, 10, and 11).

After dropping off the rider, the first self-driving vehicle 5 a can move away from the drop-off location 50 and away from the drop-off area 55 (as indicated by arrow 56 in FIG. 9).

FIG. 9 illustrates a route 92 a taken by the rider from when she was dropped off at the drop-off location 50. Locations 51 a, 51 b, 51 c, 51 d, 51 e (e.g., indoor locations and/or outdoor locations) can be determined along the route 92 a based on information from outdoor positioning systems and/or indoor positioning systems (e.g., systems 53, 54).

Positioning systems 53 (e.g., GPS satellites) can send communications 57 a, 57 b, 57 c (e.g., radio signals) to the first self-driving vehicle 5 a. The first self-driving vehicle 5 a can use these communications 57 a, 57 b, 57 c to determine positions of the first self-driving vehicle 5 a at various times (e.g., when the first self-driving vehicle 5 a drops off the rider).

The first self-driving vehicle 5 a can send communications 59 (which can include GPS coordinates of the first self-driving vehicle 5 a) to an antenna 42 via intermediary communication systems 15 a. Intermediary communication systems 15 a can send communications 60 (which can include GPS coordinates of the first self-driving vehicle 5 a) to the antenna 42. (Intermediary communication systems 15 a, 15 b, 15 c can include all of the features and systems described in the context of intermediary communication systems 15.)

Positioning systems 54 (e.g., GPS satellites, indoor positioning systems) can send communications 58 a, 58 b, 58 c (e.g., radio signals) to the remote computing device 12. The remote computing device 12 can use these communications 58 a, 58 b, 58 c to determine positions of the remote computing device 12 at various times (e.g., at a drop-off location 50, at locations 51 a, 51 b, 51 c, 51 d, 51 e, 51 f, 51 g, 51 h, 51 i, 51 j, 51 k, 51 m, 51 n, and/or at a pick-up location 51 p).

The remote computing device 12 can send communications 62 (which can include GPS coordinates of the remote computing device 12) to an antenna 42 via intermediary communication systems 15 b. Intermediary communication systems 15 b can send communications 61 (which can include GPS coordinates of the remote computing device 12) to the antenna 42.

FIG. 10 illustrates a route 92 b taken by the rider from when she was dropped off at the drop-off location 50. Locations 51 a, 51 b, 51 c, 51 d, 51 e, 51 f, 51 g, 51 h, 51 i, 51 j, 51 k (e.g., indoor locations and/or outdoor locations) can be determined along the route 92 b based on information from outdoor positioning systems and/or indoor positioning systems (e.g., systems 53, 54).

As illustrated in FIG. 11, the remote computing device 12 can return to an area 55 that includes the drop-off location 50. The rider can walk and/or be transported (e.g., via scooter, bike, train, plane or bus) along the route 92 c while having possession of the remote computing device 12. Location 51 a indicates the remote computing device 12 moving away from the drop-off location 50. Location 51 b indicates the remote computing device 12 having left the drop-off area 55. Locations 51 k, 51 m, 51 n indicate the remote computing device 12 moving toward the drop-off area 55. Location 51 p indicates the remote computing device 12 having returned to the drop-off area 55 (after being dropped off by the fleet, moving away from the drop-off location 50, and leaving the drop-off area 55).

Arrow 68 indicates the first self-driving vehicle 5 a moving toward the drop-off area 55. Arrow 65 indicates the second self-driving vehicle 5 b moving toward the drop-off area 55.

Positioning systems 53 (e.g., GPS satellites) can send communications 57 f (e.g., radio signals) to the second self-driving vehicle 5 b. The second self-driving vehicle 5 b can use these communications 57 f to determine positions of the second self-driving vehicle 5 b at various times.

FIG. 11 illustrates a diagrammatic view of the remote computing device 12 having returned to the drop-off area 55 after having left the drop-off area 55. In some embodiments, the computer system 34 is configured to prompt at least one of the first self-driving vehicle 5 a and the second self-driving vehicle 5 b to drive to an area 55 within 100 feet and/or within 250 feet of the drop-off location 50 to pick up the rider in response to determining that the second location data is indicative of the remote computing device 12 (and thus the rider) having returned to the area 55 after being dropped off.

Automatically prompting the fleet to pick up the rider (in response to detecting the remote computing device 12 having returned to an area 55 comprising the drop-off location 50) is highly convenient for the rider. The vehicle management system can compare the drop-off location 50 to a new location 51 p of the remote computing device 12 to determine if the fleet should pick up the rider.

In some cases, the vehicle management system will not have a pick-up location selected by the rider and/or will not have a pick-up time selected by the rider. The rider returning to a drop-off area, however, can prompt a self-driving vehicle 5 a to drive to the drop-off area to pick up the rider (e.g., without the rider having to tell the vehicle management system that she wants a ride).

In some embodiments, the first location data comprises a first GPS location calculated by at least one of the first self-driving vehicle 5 a and the second self-driving vehicle 5 b. The second location data can comprise a second GPS location calculated by the remote computing device 12.

In some cases, sending the fleet to pick up the rider could be wasteful if the rider does not want a ride (even though the rider has returned to the drop-off area 55). To guard against unwanted pick-up attempts, in some embodiments, the computer system 34 is configured to prompt at least one of the first self-driving vehicle 5 a and the second self-driving vehicle 5 b to drive to the area 55 in response to determining, based on the second location data, that the rider is not leaving the area 55. For example, analyzing movements of the remote computing device 12 after the remote computing device returns to the drop-off area 55 can enable the vehicle management system to determine that the rider is not leaving the area 55. In some embodiments, determining, based on the second location data, that the rider is not leaving the area 55 comprises determining that the remote computing device 12 is not moving away from at least one of the area 55 and the drop-off location 50.

Radio signals from GPS satellites can enable a vehicle to determine a first GPS location. A GPS location can include data such as 37.428499, −122.174786. In some embodiments, the first location data comprises a first GPS location based on at least a first radio signal (e.g., communication 57 a) and a second radio signal (e.g., a communication 57 b) (and in some cases based on additional radio signals 57 c) received by at least one of the first self-driving vehicle 5 a and the second self-driving vehicle 5 b.

In some embodiments, the second location data comprises a second GPS location based on at least a third radio signal and a fourth radio signal (and in some cases additional radio signals) received by the remote computing device 12.

In some embodiments, the first location data comprises a first GPS location calculated by at least one of the first self-driving vehicle 5 a and the second self-driving vehicle 5 b. The second location data can comprise a first indoor location calculated by the remote computing device 12 based on information received via radio waves (e.g., communications 58 a, 58 b, 58 c) from an indoor positioning system (e.g., positioning system 54) (as illustrated in FIG. 9). The radio waves can be broadcast by iBeacons made by Apple Inc. The positioning systems 54 can be iBeacons, GPS satellites, cell towers, WiFi emitters, or any other suitable type of positioning system.

Referring now to FIG. 11, the vehicle management system can determine that the remote computing device 12 has arrived at location 51 p, which is inside the drop-off area 55. The vehicle management system can receive the location 51 p (of the remote computing device 12) and then can compare the location 51 p to the drop-off location 50 to measure the distance between the location 51 p and the drop-off location 50 to determine if the location 51 p is within a predetermined distance of the drop-off location 50. There is a chance, however, that the rider is walking through the drop-off area 55 rather than waiting for a ride.

In some embodiments, the computer system 34 is configured to prompt at least one of the first self-driving vehicle 5 a and the second self-driving vehicle 5 b to drive to the area 55 in response to determining, based on movement data from the remote computing device 12, that the rider is not leaving the area 55. The remote computing device 12 can comprise at least one of an accelerometer 74, a gyroscope 76, and a Wi-Fi tracker 77. The movement data can be based on information from at least one of the accelerometer 74, the gyroscope 76, and the Wi-Fi tracker 77.

A Wi-Fi Tracker 77 can analyze radio waves (e.g., from indoor positioning systems) to determine if the remote computing device 12 is moving (e.g., at a speed indicative of walking). Data from the accelerometer 74 and/or gyroscope 76 can be analyzed by the remote computing device 12 and/or by the computer system 34 to determine if the movement sensed by the accelerometer 74 and/or gyroscope 76 is indicative of walking. If the rider is walking, the likelihood of the wanting a ride is lower than if the rider is standing (e.g., standing in an area 55, 64).

The remote computing device 12 can determine that movement detected by the remote computing device 12 is indicative of the remote computing device 12 not leaving the area 55. For example, lack of movement (e.g., as sensed by a compass 78, by an accelerometer 74, a gyroscope 76, and/or a Wi-Fi tracker 77) can indicate that the remote computing device 12 is not leaving the area 55. Movement that is not indicative of the movement patterns of walking and/or that is less than a movement threshold (e.g., as sensed by a compass 78, an accelerometer 74, a gyroscope 76, and/or a Wi-Fi tracker 77) can indicate that the remote computing device 12 is not leaving the area 55. The remote computing device 12 can send a communication to a computer system 34 (e.g., via intermediary communication systems 15 b) regarding movement of the remote computing device 12.

In some embodiments, the vehicle management system comprises program instructions 32 configured to be executed by the remote computing device 12 having at least one of an accelerometer 74, a gyroscope 76, and a Wi-Fi tracker 77. The program instructions 32 can be configured to cause the remote computing device 12 to send a first communication to the computer system 34 in response to the remote computing device 12 using at least one of the accelerometer 74, the gyroscope 76, and the Wi-Fi tracker 77 to determine that the rider is not moving away from at least one of the area 55 and the drop-off location 50. The computer system 34 can be configured to prompt at least one of the first self-driving vehicle 5 a and the second self-driving vehicle 5 b to drive to the area 55 in response to receiving the first communication.

In some embodiments, the program instructions 32 are configured to cause the remote computing device 12 to send a first communication to the computer system 34 in response to the remote computing device 12 using at least one of the accelerometer 74, the gyroscope 76, and the Wi-Fi tracker 77 to determine that the rider is not moving in a manner indicative of leaving the area 55. A walking motion (e.g., as sensed by accelerometer 74 and/or a gyroscope 76) can be a manner indicative of leaving the area 55. Moving in a direction away from the drop-off location 50 and/or moving in a direction away from the drop-off area 55 (e.g., as sensed by a compass 78, by an accelerometer 74, a gyroscope 76, and/or a Wi-Fi tracker 77) can be a manner indicative of leaving the area 55.

In some embodiments, the computer system is a computer system 82 (labeled in FIG. 6) that is a part of the remote computing device 12. In some embodiments, the computer system is a computer system 34 (labeled in FIGS. 5 and 11) that is not part of the remote computing device 12. In some embodiments, the computer system is a computer system 19 (labeled in FIG. 4) that is not part of the remote computing device 12. Embodiments described herein can use many different types of computer systems, which can be located in many different locations.

In some embodiments, the remote computing device 12 tells the computer system 34 that the remote computing device 12 is not moving away from the drop-off location 50 and/or leaving the drop-off area 55.

Some embodiments comprise program instructions 32 configured to be executed by the remote computing device 12 having at least one of an accelerometer 74 and a gyroscope 76. The program instructions 32 can be configured to cause the remote computing device 12 to send a first communication to the computer system 34 (e.g., a direct communication, an indirect communication) in response to the remote computing device 12 using at least one of the accelerometer 74 and the gyroscope 76 to determine that the rider is not walking. The computer system 34 can be configured to prompt at least one of the first self-driving vehicle 5 a and the second self-driving vehicle 5 b to drive to the area 55 in response to receiving the first communication.

The remote computing device 12 can use the accelerometer 74 to determine that the rider is not walking by analyzing acceleration data. Walking has an acceleration pattern that is identifiable. In addition, detection less than a threshold level of acceleration can be indicative of the rider not walking.

The remote computing device 12 can use the gyroscope 76 to determine that the rider is not walking by analyzing data from the gyroscope 76. Some gyroscopes are used as gyrocompasses for an inertial guidance system. Some gyroscopes are microelectromechanical systems (MEMS) gyroscopes. A gyroscope 76 can be used to sense direction. When combined with accelerometer data, gyroscope data can be used to determine that a rider is walking in a particular direction. In some cases, the rider returns to the drop-off location before a scheduled pick-up time.

In some embodiments, the computer system 34 comprises at least one processor 35 and a memory 31 having program instructions 32 that when executed by the at least one processor 35 cause the at least one processor 35 to automatically prompt at least one of the first self-driving vehicle 5 a and the second self-driving vehicle 5 b to drive to an area 55 within 100 feet and/or within 250 feet of the drop-off location 50 to pick up the rider prior to a scheduled pick-up time in response to determining that the second location data is indicative of the remote computing device 12 having returned to the area 55 after being dropped off.

In some cases, the rider is dropped off (by a self-driving vehicle 5 a) at the drop-off location 50, the rider moves away from the drop-off location 50, the rider leaves the drop-off area 55, and then the rider returns to the drop-off area 55.

In some embodiments, the computer system 34 comprises at least one processor 35 and a memory 31 having program instructions 32 that when executed by the at least one processor 35 are configured to cause the at least one processor 35 to prompt the first self-driving vehicle 5 a to drive to an area 55 within 100 feet and/or within 250 feet of the drop-off location 50 to pick up the rider in response to determining that the second location data is indicative of the remote computing device 12 having returned to the area 55 after being dropped off.

In some cases, the rider leaves the drop-off area 55 and then returns to the drop-off area 55. Then, however, the rider might leave the drop-off area 55 again (without being picked up by the self-driving vehicle fleet). For example, the rider might walk away from the drop-off area 55 after having returned to the drop-off area 55.

In some embodiments, after prompting the first self-driving vehicle 5 a to drive to the area 55 in response to determining that the second location data is indicative of the remote computing device 12 having returned to the area 55, the program instructions 32 are configured to cause the at least one processor 35 to prompt the first self-driving vehicle 5 a to drive away from the area 55 in response to determining that the second location data is indicative of the remote computing device 12 moving away from at least one of the drop-off location 50 and the area 55. In these embodiments, the program instructions 32 are configured to cause the at least one processor 35 to prompt the first self-driving vehicle 5 a to drive away from the area 55 (in response to determining that the second location data is indicative of the remote computing device 12 moving away from at least one of the drop-off location 50 and the area 55) after prompting the first self-driving vehicle 5 a to drive to the area 55 in response to determining that the second location data is indicative of the remote computing device 12 having returned to the area 55. In other words, the program instructions 32 can be configured prior to (A) determining that the second location data is indicative of the remote computing device 12 having returned to the area 55 and also prior to (B) prompting the first self-driving vehicle 5 a to drive away from the area 55 in response to determining that the second location data is indicative of the remote computing device 12 moving away from at least one of the drop-off location 50 and the area 55.

In some embodiments, the program instructions 32 are configured to cause the at least one processor 35 to prompt the first self-driving vehicle 5 a to drive away from the area 55 after the program instructions prompt the first self-driving vehicle 5 a to drive to the area 55. The program instructions 32 are configured to cause the at least one processor 35 to prompt the first self-driving vehicle 5 a to drive away from the area 55 in response to determining that the second location data is indicative of the remote computing device 12 moving away from at least one of the drop-off location 50 and the area 55.

A fleet of self-driving vehicles 5 a, 5 b, 5 c can drop off and pick up a rider. A first vehicle can drop off the rider and a different vehicle can later pick up the rider.

In some embodiments, the location tracking system is configured to receive the first location data indicative of the drop-off location 50 where the first self-driving vehicle 5 a dropped off the rider. The computer system 34 can be configured to prompt (e.g., via program instructions 32) the second self-driving vehicle 5 b to drive to an area 55 within 100 feet of the drop-off location 50 to pick up the rider in response to determining that the second location data is indicative of the remote computing device 12 having returned to the area 55 after being dropped off.

The second location data can include a GPS location (or any other type of location). The GPS location being inside the area 55 is indicative of the remote computing device 12 having returned to the area 55.

Referring now to FIG. 10, the computer system 34 can be configured to (e.g., due to program instructions 32) automatically prompt the first self-driving vehicle 5 a to drive to an area 55 within 100 feet and/or within 250 feet of the drop-off location 50 to pick up the rider in response to determining that the second location data is indicative of the remote computing device 12 moving toward the area 55. For example, locations 51 j, 51 k along the route 92 b are indicative of the remote computing device 12 moving toward the area 55 (as indicated by arrow 67).

Locations 51 j, 51 k indicating that the remote computing device 12 is moving toward the area 55 may cause unwanted pick-up attempts. For example, the rider may simply walk 50 feet toward the pick-up area 55 and then turn another direction within a retail store. Moving toward the pick-up area 55 plus moving toward the pick-up area 55 for at least 30 seconds, at least 2 minutes and/or at least 4 minutes can be a more certain indicator of the rider actually being in route to the pick-up area 55.

In some embodiments, the predetermined amount of time (during which the rider must be moving toward the pick-up area 55) scales with a distance from the remote computing device 12 to at least one of the drop-off location 50 and the drop-off area 55. For example, if the rider is one mile away from the drop-off location 50 (and/or the drop-off area 55) the predetermined amount of time can be five minutes. If the rider is only 500 feet away from the drop-off location 50 (and/or the drop-off area 55) the predetermined amount of time can be thirty seconds.

In some embodiments, the computer system 34 is configured to automatically prompt the first self-driving vehicle 5 a to drive to an area 55 within 100 feet and/or within 250 feet of the drop-off location 50 to pick up the rider in response to determining that the second location data is indicative of the remote computing device 12 moving toward the area 55 for at least a predetermined amount of time.

Some embodiments minimize (or even eliminate) unwanted pick-up attempts by the detection of certain events causing the system to ask the rider if she wants a ride.

In some embodiments, the computer system 34 comprises at least one processor 35 and a memory 31 having program instructions 32 that when executed by the at least one processor 35 are configured to cause the at least one processor 35 to cause the remote computing device 12 to prompt the rider to at least one of request a ride, confirm the rider wants the ride, cancel a pending pick up, enter a pick-up time, and enter a pick-up location.

The remote computing device can use an “app” to ask the rider to enter a pick-up time and/or enter a pick-up location.

The program instructions 32 can be configured to cause the at least one processor 35 to cause the remote computing device 12 to prompt the rider in response to determining that the second location data is indicative of the remote computing device 12 moving toward an area 55 within 100 feet and/or within 250 feet of the drop-off location 50.

If a self-driving vehicle 5 a is very close to a pick-up location, the vehicle management system can avoid prompting the self-driving vehicle 5 a to drive to the pick-up location until the rider has arrived at the pick-up location (or at least the vehicle management system is very sure about when the rider will arrive at the pick-up location and/or is very sure the rider actually wants a ride from the fleet).

In some cases, however, the nearest self-driving vehicle 5 a is far away (e.g., as measured in travel time and/or in distance) from the pick-up location and/or from the rider. In these cases, the vehicle management system can be more likely to prompt the self-driving vehicle 5 a to start moving toward the pick-up location and/or toward the rider (than is often the case when the nearest self-driving vehicle 5 a is very close to the pick-up location). For example, the vehicle management system could prompt the self-driving vehicle 5 a to start moving toward the pick-up location (even though the vehicle management system is unsure if the rider actually wants a ride) to avoid making the rider wait a long time for a ride (in the event that the rider actually wants a ride).

In some embodiments, at least one of the computer system 34 and the remote computing device 12 is configured to estimate a first amount of time that the first self-driving vehicle 5 a is away from a first area 55 within 100 feet, within 900 feet, and/or within one mile of the drop-off location 50. At least one of the computer system 34 and the remote computing device 12 can be configured to estimate a second amount of time that the remote computing device 12 is away from a second area 55 within 100 feet, within 900 feet, and/or within 0.4 miles of the drop-off location 50. The computer system 34 can be configured to automatically prompt the first self-driving vehicle 5 a to drive to a third area 55 within 100 feet and/or within 250 feet of the drop-off location 50 to pick up the rider in response to determining that the second location data is indicative of the remote computing device 12 moving toward the area 55 and in response to determining that the first amount of time is at least fifty percent of the second amount of time.

The computer system 34 can be configured to automatically prompt the first self-driving vehicle 5 a to drive to a third area 55 within 100 feet and/or within 250 feet of the drop-off location 50 to pick up the rider in response to determining that the second location data is indicative of the remote computing device 12 moving toward the area 55 and in response to determining that the second amount of time is at least one of less than the first amount of time and less than fifty percent greater than the first amount of time.

The computer system 34 can be configured to automatically prompt the first self-driving vehicle 5 a to drive to a third area 55 within 100 feet and/or within 250 feet of the drop-off location 50 to pick up the rider in response to determining that the second location data is indicative of the remote computing device 12 moving toward the area 55 and in response to determining that the first amount of time is at least one of greater than the second amount of time and at least fifty percent of the second amount of time.

In some embodiments, each system comprises at least one processor and a memory comprising program instructions that when executed by the at least one processor cause the system to perform method steps.

Some embodiments comprise using a vehicle management system comprising a self-driving vehicle fleet having at least one of a first self-driving vehicle 5 a and a second self-driving vehicle 5 b. The fleet can be configured to transport a rider.

Some embodiments comprise receiving, by the vehicle management system, a first location data indicative of a drop-off location 50 where the self-driving vehicle fleet dropped off the rider. Some embodiments comprise receiving, by the vehicle management system, a second location data indicative of at least one location (e.g., locations 51 a, 51 b, 51 c, 51 d, 51 e, 51 f, 51 g, 51 h, 51 i, 51 j, 51 k, 51 m, 51 n) of a remote computing device 12 of the rider during at least a portion of a period from after when the self-driving vehicle fleet drops off the rider to before when the self-driving vehicle fleet picks up the rider.

Some embodiments comprise prompting, by the vehicle management system, the first self-driving vehicle 5 a to drive to an area 55 within 100 feet and/or within 250 feet of the drop-off location 50 to pick up the rider in response to determining (e.g., by at least one of the vehicle management system, a computer system 34, a self-driving vehicle, and the remote computing device 12) that the second location data is indicative of the remote computing device 12 having returned to the area 55 after being dropped off.

In some embodiments, the first location data comprises a first GPS location (e.g., of the drop-off location 50) calculated by at least one of the first self-driving vehicle 5 a and the second self-driving vehicle 5 b. The second location data can comprise a second GPS location (e.g., one of locations 51 a, 51 b, 51 c, 51 d, 51 e, 51 f, 51 g, 51 h, 51 i, 51 j, 51 k, 51 m, 51 n) calculated by the remote computing device 12.

Some embodiments comprise prompting (e.g., by at least one of the vehicle management system, a computer system 34, a self-driving vehicle, and the remote computing device 12) the first self-driving vehicle 5 a to drive to the area 55 in response to determining, based on the second location data, (e.g., by at least one of the vehicle management system, a computer system 34, a self-driving vehicle, and the remote computing device 12) that the remote computing device 12 is not moving away from at least one of the area 55 and the drop-off location 50.

In some embodiments, the remote computing device 12 comprises at least one of an accelerometer 74 and a gyroscope 76. Some embodiments comprise using at least one of the accelerometer 74 and the gyroscope 76 to collect movement data. Some embodiments comprise prompting (e.g., by the vehicle management system) the first self-driving vehicle 5 a to drive to the area 55 in response to determining, (e.g., by the vehicle management system) based on the movement data, that the rider is not at least one of moving away from the area 55, moving away from the drop-off location 50, and moving more than a predetermined threshold. (Moving more than a predetermined threshold can be indicative of walking.)

In some embodiments, after prompting (e.g., by at least one of the vehicle management system, a computer system 34, a self-driving vehicle, and the remote computing device 12) the self-driving vehicle to drive to the area 55 in response to determining that the second location data is indicative of the remote computing device 12 having returned to the area 55, some embodiments comprise prompting (e.g., by the vehicle management system) the first self-driving vehicle 5 a to drive away from the area 55 in response to determining (e.g., by at least one of the vehicle management system, a computer system 34, a self-driving vehicle, and the remote computing device 12) that the second location data is indicative of the remote computing device 12 moving away from at least one of the drop-off location 50 and the area 55.

Some embodiments comprise prompting (e.g., by the vehicle management system) the first self-driving vehicle 5 a to drive to an area 55 within 100 feet and/or within 250 feet of the drop-off location 50 to pick up the rider in response to determining (e.g., by at least one of the vehicle management system, a computer system 34, a self-driving vehicle, and the remote computing device 12) that the second location data is indicative of the remote computing device 12 moving toward the area 55.

Some embodiments comprise prompting, by the remote computing device 12, the rider to at least one of request a ride, confirm the rider wants the ride, cancel a pending pick up, enter a pick-up time, and enter a pick-up location, wherein the prompting is in response to determining (e.g., by at least one of the vehicle management system, the vehicle, and the remote computing device 12) that the second location data is indicative of the remote computing device 12 moving toward an area 55 within 100 feet and/or within 250 feet of the drop-off location 50.

Prompting, by the remote computing device 12, the rider to request a ride can comprise sending a push notification to the remote computing device 12. The push notification can be configured to encourage the rider to indicate if she wants a ride. The rider can use an “app” on the remote computing device 12 to select that the rider wants a ride.

Some embodiments comprise estimating (e.g., by at least one of the vehicle management system, a computer system 34, a self-driving vehicle, and the remote computing device 12) a first amount of time that the first self-driving vehicle 5 a is away from a first area 55 within 100 feet, within 250 feet, within one mile, and/or within three miles of the drop-off location 50. Some embodiments comprise estimating (e.g., by at least one of the vehicle management system, a computer system 34, a self-driving vehicle, and the remote computing device 12) a second amount of time that the remote computing device 12 is away from a second area 55 within 100 feet and/or within 250 feet of the drop-off location 50.

Some embodiments comprise prompting (e.g., by at least one of the vehicle management system, the computer system 34, a self-driving vehicle, and the remote computing device 12) the first self-driving vehicle 5 a to drive to a third area 55 within 100 feet and/or within 250 feet of the drop-off location 50 to pick up the rider in response to determining that the second location data is indicative of the remote computing device 12 moving toward the area 55 and in response to determining that the first amount of time is at least fifty percent of the second amount of time.

In some embodiments, a memory 31 of the vehicle management system comprises a pick-up time (e.g., data indicating a time of day) chosen by the rider. Some embodiments comprise overriding the pick-up time previously chosen by the rider in response to determining (e.g., by the vehicle management system) that the second location data is indicative of the remote computing device 12 having returned to the area 55. Some embodiments comprise overriding the pick-up time previously chosen by the rider in response to determining (e.g., by the vehicle management system) that the second location data is indicative of the remote computing device 12 moving towards the area 55.

In some embodiments, the computer system 34 comprises at least one processor 35 and a memory 31 having program instructions 32 that when executed by the at least one processor 35 are configured to estimate a first amount of time that the first self-driving vehicle 5 a is away from a first area 55 within 100 feet and/or within two miles of the drop-off location 50, and estimate a second amount of time that the remote computing device 12 is away from a second area 55 within feet and/or within 500 feet of the drop-off location 50. The computer system 34 can be configured to automatically prompt the first self-driving vehicle 5 a to drive to a third area 55 within feet and/or within 250 feet of the drop-off location 50 to pick up the rider in response to determining that the second location data is indicative of the rider moving toward the area 55 and/or in response to determining that the second amount of time is within plus or minus fifty percent of the first amount of time.

Interpretation

To reduce unnecessary redundancy, not every element or feature is described in the context of every embodiment, but all elements and features described in the context of any embodiment herein and/or incorporated by reference can be combined with any elements and/or features described in the context of any other embodiments.

The self-driving vehicle can be any suitable vehicle. For example, the self-driving vehicle can be a Tesla Model S made by Tesla, Inc. The Tesla Model S can include the Enhanced Autopilot package and the Full Self-Driving Capability package. The Full Self-Driving Capability package includes eight active cameras to enable full self-driving in almost all circumstances.

The self-driving vehicle can also be a Waymo car. Waymo was formerly the Google self-driving car project. Waymo, which is owned by Alphabet Inc., has logged thousands of self-driving miles over many years. Waymo vehicles have sensors and software that are designed to detect pedestrians, cyclists, vehicles, roadwork and more from a distance of up to two football fields away in all directions. Waymo has stated that its software leverages over four million miles of real-world driving data. In some embodiments, self-driving vehicles sometimes drive themselves, sometimes are driven remotely by a computer system, and sometimes are driven manually by a human turning a steering wheel, operating pedals, and performing other driver functions. In several embodiments, a self-driving vehicle drives without a human inside the vehicle to pick up the human and then lets the human drive the vehicle. Although in some cases, the human may choose not to drive the vehicle and instead may allow the vehicle to drive itself (e.g., steer and control speed) (e.g., in response to a destination requested by the human).

A remote computing device can be a smartphone, a tablet computer, a laptop computer, a desktop computer, a server, augmented reality glasses, an implanted computer, and/or any type of computer. A rider can bring her remote computing device into the self-driving vehicle, use her remote computing device in the self-driving vehicle, and leave the self-driving vehicle with her remote computing device. In some embodiments, the rider requests a ride at her home with a remote computing device, but then leaves the remote computing device at home when she goes to get a ride from the self-driving vehicle.

In some embodiments, the remote computing device is an iPhone made by Apple Inc. or an Android phone based on software made by Alphabet Inc. The remote computing device can comprise a speaker configured to emit sounds, a microphone configured to record sounds, and a display screen configured to display images. The remote computing device can comprise a battery configured to provide electrical power to operate the remote computing device.

The phrase “communicatively coupling” can include any type of direct and/or indirect coupling between various items including, but not limited to, a self-driving vehicle, a remote computing device, and a vehicle management system. For example, a remote computing device can be communicatively coupled to a vehicle management system via servers, the Cloud, the Internet, satellites, Wi-Fi networks, cellular networks, and any other suitable communication means.

Some of the devices, systems, embodiments, and processes use computers. Each of the routines, processes, methods, and algorithms described in the preceding sections may be embodied in, and fully or partially automated by, code modules executed by one or more computers, computer processors, or machines configured to execute computer instructions. The code modules may be stored on any type of non-transitory computer-readable storage medium or tangible computer storage device, such as hard drives, solid state memory, flash memory, optical disc, and/or the like. The processes and algorithms may be implemented partially or wholly in application-specific circuitry. The results of the disclosed processes and process steps may be stored, persistently or otherwise, in any type of non-transitory computer storage such as, e.g., volatile or non-volatile storage.

The term “app”, as used in this disclosure, refers to both native apps and mobile cloud apps (and Web apps). Native apps can be installed directly on remote computing devices, whereby developers can create separate app versions for each type of remote computing device (e.g., iPhone devices and Android devices). Native apps may be stored on the remote computing device out of the box, or the native apps can be downloaded from a public or private app store and installed on the remote computing device. Self-driving vehicle data associated with native apps can be stored on the remote computing device and/or can be stored remotely and accessed by the native app. Internet connectivity may be used by some instances of apps. Other instances of apps may not use Internet connectivity. In some embodiments, apps can function without Internet connectivity.

Mobile cloud apps are very similar to Web-based apps. The main similarity is that both mobile cloud apps and Web apps run on servers external to the remote computing device and may require the use of a browser on the remote computing device to display and then use the app user interface (UI). Mobile cloud apps can be native apps rebuilt to run in the mobile cloud; custom apps developed for mobile devices; or third-party apps downloaded to the cloud from external sources. Some organizations offer both a native and mobile cloud versions of their applications. In short, the term “app” refers to both native apps and mobile cloud apps.

None of the steps described herein is essential or indispensable. Any of the steps can be adjusted or modified. Other or additional steps can be used. Any portion of any of the steps, processes, structures, and/or devices disclosed or illustrated in one embodiment, flowchart, or example in this specification can be combined or used with or instead of any other portion of any of the steps, processes, structures, and/or devices disclosed or illustrated in a different embodiment, flowchart, or example. The embodiments and examples provided herein are not intended to be discrete and separate from each other.

The section headings and subheadings provided herein are nonlimiting. The section headings and subheadings do not represent or limit the full scope of the embodiments described in the sections to which the headings and subheadings pertain. For example, a section titled “Topic 1” may include embodiments that do not pertain to Topic 1 and embodiments described in other sections may apply to and be combined with embodiments described within the “Topic 1” section.

Some of the devices, systems, embodiments, and processes use computers. Each of the routines, processes, methods, and algorithms described in the preceding sections may be embodied in, and fully or partially automated by, code modules executed by one or more computers, computer processors, or machines configured to execute computer instructions. The code modules may be stored on any type of non-transitory computer-readable storage medium or tangible computer storage device, such as hard drives, solid state memory, flash memory, optical disc, and/or the like. The processes and algorithms may be implemented partially or wholly in application-specific circuitry. The results of the disclosed processes and process steps may be stored, persistently or otherwise, in any type of non-transitory computer storage such as, e.g., volatile or non-volatile storage.

The various features and processes described above may be used independently of one another, or may be combined in various ways. All possible combinations and subcombinations are intended to fall within the scope of this disclosure. In addition, certain method, event, state, or process blocks may be omitted in some implementations. The methods, steps, and processes described herein are also not limited to any particular sequence, and the blocks, steps, or states relating thereto can be performed in other sequences that are appropriate. For example, described tasks or events may be performed in an order other than the order specifically disclosed. Multiple steps may be combined in a single block or state. The example tasks or events may be performed in serial, in parallel, or in some other manner. Tasks or events may be added to or removed from the disclosed example embodiments. The example systems and components described herein may be configured differently than described. For example, elements may be added to, removed from, or rearranged compared to the disclosed example embodiments.

Conditional language used herein, such as, among others, “can,” “could,” “might,” “may,” “e.g.,” and the like, unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that features, elements and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without author input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular embodiment. The terms “comprising,” “including,” “having,” and the like are synonymous and are used inclusively, in an open-ended fashion, and do not exclude additional elements, features, acts, operations and so forth. Also, the term “or” is used in its inclusive sense (and not in its exclusive sense) so that when used, for example, to connect a list of elements, the term “or” means one, some, or all of the elements in the list. Conjunctive language such as the phrase “at least one of X, Y, and Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to convey that an item, term, etc. may be either X, Y, or Z. Thus, such conjunctive language is not generally intended to imply that certain embodiments require at least one of X, at least one of Y, and at least one of Z to each be present.

The term “and/or” means that “and” applies to some embodiments and “or” applies to some embodiments. Thus, A, B, and/or C can be replaced with A, B, and C written in one sentence and A, B, or C written in another sentence. A, B, and/or C means that some embodiments can include A and B, some embodiments can include A and C, some embodiments can include B and C, some embodiments can only include A, some embodiments can include only B, some embodiments can include only C, and some embodiments can include A, B, and C. The term “and/or” is used to avoid unnecessary redundancy.

While certain example embodiments have been described, these embodiments have been presented by way of example only, and are not intended to limit the scope of the inventions disclosed herein. Thus, nothing in the foregoing description is intended to imply that any particular feature, characteristic, step, module, or block is necessary or indispensable. Indeed, the novel methods and systems described herein may be embodied in a variety of other forms; furthermore, various omissions, substitutions, and changes in the form of the methods and systems described herein may be made without departing from the spirit of the inventions disclosed herein.

Claims (30)

The following is claimed:
1. A vehicle management system comprising:
a self-driving vehicle fleet comprising at least one of a first self-driving vehicle and a second self-driving vehicle, wherein the fleet is configured to transport a rider;
a computer system comprising at least one computer and configured to be communicatively coupled with a remote computing device of the rider and at least one of the first self-driving vehicle and the second self-driving vehicle; and
a location tracking system communicatively coupled with the computer system and configured to receive a first location data indicative of a drop-off location where the self-driving vehicle fleet dropped off the rider,
wherein the location tracking system is configured to receive a second location data indicative of at least one location of the remote computing device during a period of time from after when the self-driving vehicle fleet drops off the rider to before when the self-driving vehicle fleet picks up the rider, and
wherein the computer system is configured to prompt at least one of the first self-driving vehicle and the second self-driving vehicle to drive to an area within a predetermined distance of the drop-off location to pick up the rider in response to determining that the second location data is indicative of the remote computing device having returned to the area after being dropped off.
2. The vehicle management system of claim 1, wherein the first location data comprises a first GPS location calculated by at least one of the first self-driving vehicle and the second self-driving vehicle, and the second location data comprises a second GPS location calculated by the remote computing device.
3. The vehicle management system of claim 2, wherein the computer system is configured to prompt at least one of the first self-driving vehicle and the second self-driving vehicle to drive to the area in response to determining, based on the second location data, that the rider is not moving away from at least one of the area and the drop-off location.
4. The vehicle management system of claim 1, wherein the first location data comprises a first GPS location based on a first radio signal and a second radio signal received by at least one of the first self-driving vehicle and the second self-driving vehicle, and
the second location data comprises a second GPS location based on a third radio signal and a fourth radio signal received by the remote computing device.
5. The vehicle management system of claim 1, wherein the first location data comprises a first GPS location calculated by at least one of the first self-driving vehicle and the second self-driving vehicle, and the second location data comprises a first indoor location calculated by the remote computing device based on information received via radio waves from an indoor positioning system.
6. The vehicle management system of claim 1, wherein the computer system is configured to prompt at least one of the first self-driving vehicle and the second self-driving vehicle to drive to the area in response to determining, based on movement data from the remote computing device, that the rider is not moving away from at least one of the area and the drop-off location,
wherein the remote computing device comprises at least one of an accelerometer, a gyroscope, and a Wi-Fi tracker, and the movement data is based on information from at least one of the accelerometer, the gyroscope, and the Wi-Fi tracker.
7. The vehicle management system of claim 1, further comprising program instructions configured to be executed by the remote computing device having at least one of an accelerometer, a gyroscope, and a Wi-Fi tracker, wherein the program instructions are configured to cause the remote computing device to send a first communication to the computer system in response to the remote computing device using at least one of the accelerometer, the gyroscope, and the Wi-Fi tracker to determine that the rider is not moving away from at least one of the area and the drop-off location,
wherein the computer system is configured to prompt at least one of the first self-driving vehicle and the second self-driving vehicle to drive to the area in response to receiving the first communication.
8. The vehicle management system of claim 1, further comprising program instructions configured to be executed by the remote computing device having at least one of an accelerometer and a gyroscope, wherein the program instructions are configured to cause the remote computing device to send a first communication to the computer system in response to the remote computing device using at least one of the accelerometer and the gyroscope to determine that the rider is not walking,
wherein the computer system is configured to prompt at least one of the first self-driving vehicle and the second self-driving vehicle to drive to the area in response to receiving the first communication.
9. The vehicle management system of claim 1, wherein the computer system comprises at least one processor and a memory having program instructions that when executed by the at least one processor cause the at least one processor to automatically prompt at least one of the first self-driving vehicle and the second self-driving vehicle to drive to the area within the predetermined distance of the drop-off location to pick up the rider prior to a scheduled pick-up time in response to determining that the second location data is indicative of the remote computing device having returned to the area after being dropped off.
10. The vehicle management system of claim 1, wherein the predetermined distance is 250 feet, and the computer system comprises at least one processor and a memory having program instructions that when executed by the at least one processor are configured to cause the at least one processor to prompt the first self-driving vehicle to drive to the area within 250 feet of the drop-off location to pick up the rider in response to determining that the second location data is indicative of the remote computing device having returned to the area after being dropped off.
11. The vehicle management system of claim 10, wherein after prompting the first self-driving vehicle to drive to the area in response to determining that the second location data is indicative of the remote computing device having returned to the area, the program instructions are configured to cause the at least one processor to prompt the first self-driving vehicle to drive away from the area in response to determining that the second location data is indicative of the remote computing device moving away from at least one of the drop-off location and the area.
12. The vehicle management system of claim 1, wherein the predetermined distance is 250 feet, and the location tracking system is configured to receive the first location data indicative of the drop-off location where the first self-driving vehicle dropped off the rider,
wherein the computer system is configured to prompt the second self-driving vehicle to drive to the area within 250 feet of the drop-off location to pick up the rider in response to determining that the second location data is indicative of the remote computing device having returned to the area after being dropped off.
13. The vehicle management system of claim 1, wherein the predetermined distance is 100 feet, and the computer system is configured to automatically prompt the first self-driving vehicle to drive toward the area within 100 feet of the drop-off location to pick up the rider in response to determining that the second location data is indicative of the remote computing device moving toward the area.
14. The vehicle management system of claim 1, wherein the predetermined distance is 250 feet, and the computer system is configured to automatically prompt the first self-driving vehicle to drive toward the area within 250 feet of the drop-off location to pick up the rider in response to determining that the second location data is indicative of the remote computing device moving toward the area for at least a predetermined amount of time.
15. The vehicle management system of claim 1, wherein the predetermined distance is 250 feet, and
the computer system comprises at least one processor and a memory having program instructions that when executed by the at least one processor are configured to, in response to determining that the second location data is indicative of the remote computing device moving toward the area within 250 feet of the drop-off location, cause the at least one processor to cause the remote computing device to prompt the rider to at least one of request a ride, confirm the rider wants the ride, cancel a pending pick up, enter a pick-up time, and enter a pick-up location.
16. The vehicle management system of claim 1, wherein the area is a first area, and the predetermined distance is a first predetermined distance,
wherein at least one of the computer system and the remote computing device are configured to estimate a first amount of time that the first self-driving vehicle is away from a second area within a second predetermined distance of the drop-off location and estimate a second amount of time that the remote computing device is away from a third area within a third predetermined distance of the drop-off location,
wherein the computer system is configured to automatically prompt the first self-driving vehicle to drive toward a fourth area within a fourth predetermined distance of the drop-off location in response to determining that the second location data is indicative of the remote computing device moving toward the first area and in response to determining that the first amount of time is at least fifty percent of the second amount of time.
17. The vehicle management system of claim 1, wherein the computer system comprises a memory, and
the computer system is configured to automatically prompt the first self-driving vehicle to drive to the area to pick up the rider in response to determining that the second location data is indicative of the remote computing device having returned to the area.
18. The vehicle management system of claim 17, wherein the memory comprises a pick-up time selected by the rider, the computer system is configured to override the pick-up time by prompting the first self-driving vehicle to drive to the area to pick up the rider prior to the pick-up time, and the computer system is configured to override the pick-up time in response to determining that the second location data is indicative of the remote computing device having arrived at the area prior to the pick-up time.
19. The vehicle management system of claim 1, wherein the computer system comprises at least one processor and a memory having a pick-up time selected by the rider,
wherein the memory comprises program instructions that when executed by the at least one processor are configured to, in response to determining that the second location data is indicative of the remote computing device having returned to the area prior to the pick-up time, cause the at least one processor to cause the remote computing device to prompt the rider to at least one of request a ride, confirm the rider wants the ride, and cancel a pending pick up.
20. A method of using a vehicle management system comprising a self-driving vehicle fleet having at least one of a first self-driving vehicle and a second self-driving vehicle, wherein the fleet is configured to transport a rider, the method comprising:
receiving, by the vehicle management system, a first location data indicative of a drop-off location where the self-driving vehicle fleet dropped off the rider;
receiving, by the vehicle management system, a second location data indicative of at least one location of a remote computing device of the rider during a period of time from after when the self-driving vehicle fleet drops off the rider to before when the self-driving vehicle fleet picks up the rider, and
prompting, by the vehicle management system, the first self-driving vehicle to drive to an area within 250 feet of the drop-off location to pick up the rider in response to determining that the second location data is indicative of the remote computing device having returned to the area after being dropped off.
21. The method of claim 20, wherein the first location data comprises a first GPS location calculated by at least one of the first self-driving vehicle and the second self-driving vehicle, and the second location data comprises a second GPS location calculated by the remote computing device,
the method further comprising prompting the first self-driving vehicle to drive to the area in response to determining, based on the second location data, that the remote computing device is not moving away from at least one of the area and the drop-off location.
22. The method of claim 20, wherein the remote computing device comprises at least one of an accelerometer and a gyroscope, the method further comprising:
using at least one of the accelerometer and the gyroscope to collect movement data, and
prompting the first self-driving vehicle to drive to the area in response to determining, based on the movement data, that the rider is not at least one of moving away from the area, moving away from the drop-off location, and moving more than a predetermined threshold.
23. The method of claim 20, wherein after prompting the first self-driving vehicle to drive to the area in response to determining that the second location data is indicative of the remote computing device having returned to the area, the method further comprising:
prompting the first self-driving vehicle to drive away from the area in response to determining that the second location data is indicative of the remote computing device moving away from at least one of the drop-off location and the area.
24. The method of claim 20, further comprising prompting the first self-driving vehicle to drive toward the area within 250 feet of the drop-off location to pick up the rider in response to determining that the second location data is indicative of the remote computing device moving toward the area.
25. The method of claim 20, further comprising prompting, by the remote computing device, the rider to at least one of request a ride, confirm the rider wants the ride, cancel a pending pick up, enter a pick-up time, and enter a pick-up location,
wherein the prompting is in response to determining that the second location data is indicative of the remote computing device moving toward the area within 250 feet of the drop-off location.
26. The method of claim 20, wherein the area is a first area, the method further comprising:
estimating a first amount of time that the first self-driving vehicle is away from a second area within a first predetermined distance of the drop-off location,
estimating a second amount of time that the remote computing device is away from a third area within a second predetermined distance of the drop-off location, and
prompting the first self-driving vehicle to drive toward a fourth area within a third predetermined distance of the drop-off location in response to determining that the second location data is indicative of the remote computing device moving toward the first area and in response to determining that the first amount of time is at least fifty percent of the second amount of time.
27. The method of claim 20, wherein a memory of the vehicle management system comprises a pick-up time chosen by the rider, the method further comprising:
prompting, by the remote computing device, the rider to at least one of request a ride, confirm the rider wants the ride, and cancel a pending pick up, wherein the prompting is in response to determining that the second location data is indicative of the remote computing device having returned to the area prior to the pick-up time.
28. The method of claim 20, wherein the first location data comprises a first GPS location, and the second location data comprises a second GPS location and a third GPS location,
after prompting the first self-driving vehicle to drive to the area in response to determining that the second GPS location is indicative of the remote computing device having returned to the area and prior to picking up the rider, the method further comprising:
prompting the first self-driving vehicle to drive away from the area in response to determining that the third GPS location is indicative of the remote computing device moving away from at least one of the drop-off location and the area.
29. The vehicle management system of claim 1, wherein the computer system comprises a memory having a third location data indicative of a pick-up location selected by the rider, wherein the pick-up location is not located in the area,
wherein the computer system is configured to override the pick-up location selected by the rider by automatically prompting the first self-driving vehicle to drive to the area to pick up the rider in response to determining that the second location data is indicative of the remote computing device having returned to the area.
30. The vehicle management system of claim 1, wherein the computer system comprises at least one processor and a memory,
wherein the memory comprises program instructions that when executed by the at least one processor are configured to, in response to determining that the second location data is indicative of the remote computing device having returned to the area, cause the at least one processor to cause the remote computing device to prompt the rider to at least one of request a ride, confirm the rider wants the ride, and cancel a pending pick up.
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Citations (161)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4212069A (en) 1976-08-31 1980-07-08 Baumann Dwight M Paratransit fare computation and dispatching method
US5769471A (en) 1995-09-04 1998-06-23 Aisin Seiki Kabushiki Kaisha Apparatus for unlocking a door lock for a vehicle
US5798695A (en) 1997-04-02 1998-08-25 Northrop Grumman Corporation Impaired operator detection and warning system employing analysis of operator control actions
US5871063A (en) 1997-01-22 1999-02-16 Automotive Systems Laboratory, Inc. Seat belt latch sensor system
US5945919A (en) 1996-05-30 1999-08-31 Trimble Navigation Limited Dispatcher free vehicle allocation system
US5960523A (en) 1998-08-25 1999-10-05 Breed Automotive Technology, Inc. Seat belt buckle sensor
US5986420A (en) 1996-11-11 1999-11-16 Toyota Shatai Kabushiki Kaisha Apparatus for automatically opening and closing pop-up door of a vehicle
US6011478A (en) 1997-05-08 2000-01-04 Nittan Company, Limited Smoke sensor and monitor control system
US6081088A (en) 1997-12-26 2000-06-27 Asmo Co., Ltd. Automatic opening/closing apparatus
US20020077876A1 (en) 2000-12-18 2002-06-20 O'meara Cian E. Allocation of location-based orders to mobile agents
US20020121291A1 (en) 2001-03-03 2002-09-05 Daum Wolfgang R.A. Method and device to clean the interior room of a car
US20030195696A1 (en) 1993-05-18 2003-10-16 Jones M. Kelly Notification systems and methods with notifications based upon prior stop locations
US20040068354A1 (en) 1998-04-27 2004-04-08 Tabe Joseph A. Smart seatbelt control system
US20040219933A1 (en) 2003-02-07 2004-11-04 Johnathan David Faith Transportation ordering system
US7093515B2 (en) 2002-10-09 2006-08-22 Nissan Motor Co., Ltd. Accelerator pedal device for a vehicle
US20070096447A1 (en) 2003-10-07 2007-05-03 Tabe Joseph A Smart seatbelt control system
US20070132567A1 (en) 2000-03-02 2007-06-14 Donnelly Corporation Video mirror system suitable for use in a vehicle
US20070198144A1 (en) 2005-10-21 2007-08-23 Norris William R Networked multi-role robotic vehicle
US7298250B2 (en) 2003-08-29 2007-11-20 Mitsubishi Jidosha Kogyo Kabushiki Kaisha Seatbelt reminder system
US20080030906A1 (en) 2005-03-01 2008-02-07 Fujitsu Limited Magnetoresistive effect element and magnetic memory device
US20080144944A1 (en) 1992-05-05 2008-06-19 Automotive Technologies International, Inc. Neural Network Systems for Vehicles
US7413357B2 (en) 2005-06-13 2008-08-19 Silverstate Safety Image Concealed camera
US20090140886A1 (en) 2007-12-03 2009-06-04 International Truck Intellectual Property Company, Llc Multiple geofence system for vehicles
US20090287367A1 (en) 2008-05-16 2009-11-19 Gm Global Technology Operations, Inc. Method and apparatus for driver control of a limited-ability autonomous vehicle
US7698078B2 (en) 2007-06-15 2010-04-13 Tesla Motors, Inc. Electric vehicle communication interface
US7777619B2 (en) 2007-04-11 2010-08-17 Ford Global Technologies, Llc System and method for implementing active safety counter measures for an impaired driver
US20110059341A1 (en) 2008-06-12 2011-03-10 Junichi Matsumoto Electric vehicle
US20110098017A1 (en) 2007-06-27 2011-04-28 Ford Global Technologies, Llc Method And System For Emergency Notification
US7999701B1 (en) * 2008-06-26 2011-08-16 Bin Xu Transportation notification system
US8078359B2 (en) 2009-10-05 2011-12-13 Tesla Motors, Inc. User configurable vehicle user interface
US20120009845A1 (en) 2010-07-07 2012-01-12 Juniper Holding Corp. Configurable location-aware toy capable of communicating with like toys and associated system infrastructure for communicating with such toys
US20120083960A1 (en) 2010-10-05 2012-04-05 Google Inc. System and method for predicting behaviors of detected objects
US8180379B2 (en) 2007-06-28 2012-05-15 Apple Inc. Synchronizing mobile and vehicle devices
US20120158251A1 (en) 2007-05-01 2012-06-21 Ronald Van Houtan Enhanced seat belt/accelerator behavioral system
US8325025B2 (en) 2008-12-12 2012-12-04 Gordon*Howard Associates, Inc. Automated geo-fence boundary configuration and activation
US20130085817A1 (en) 2011-09-29 2013-04-04 Michael Collins Pinkus Discount offer system and method for use with for hire vehicles
US8433934B1 (en) 2012-06-28 2013-04-30 Google Inc. Saving battery on wireless connections on mobile devices using internal motion detection
US20130132140A1 (en) 2009-12-04 2013-05-23 Uber Technologies, Inc. Determining a location related to on-demand services through use of portable computing devices
US20130197674A1 (en) 2012-01-30 2013-08-01 Apple Inc. Automatic configuration of self-configurable environments
US20130231824A1 (en) 2012-03-05 2013-09-05 Florida A&M University Artificial Intelligence Valet Systems and Methods
US20130246301A1 (en) 2009-12-04 2013-09-19 Uber Technologies, Inc. Providing user feedback for transport services through use of mobile devices
US8700251B1 (en) 2012-04-13 2014-04-15 Google Inc. System and method for automatically detecting key behaviors by vehicles
US20140129132A1 (en) 2011-07-05 2014-05-08 Toyota Jidosha Kabushiki Kaisha Recommendation information provision system
US20140129951A1 (en) 2012-11-08 2014-05-08 Uber Technologies, Inc. Providing on-demand services through use of portable computing devices
US20140172727A1 (en) 2005-12-23 2014-06-19 Raj V. Abhyanker Short-term automobile rentals in a geo-spatial environment
US8818608B2 (en) 2012-11-30 2014-08-26 Google Inc. Engaging and disengaging for autonomous driving
US8849494B1 (en) 2013-03-15 2014-09-30 Google Inc. Data selection by an autonomous vehicle for trajectory modification
US20140316616A1 (en) 2013-03-11 2014-10-23 Airphrame, Inc. Unmanned aerial vehicle and methods for controlling same
US20140336935A1 (en) 2013-05-07 2014-11-13 Google Inc. Methods and Systems for Detecting Weather Conditions Using Vehicle Onboard Sensors
US20140350855A1 (en) 2012-02-28 2014-11-27 Google Inc. Systems and Methods for Providing Navigational Assistance to Reserved Parking Locations
US20150012833A1 (en) 2013-07-02 2015-01-08 Fortis Riders Corporation Mobile application using gestures to facilitate communication
US8949016B1 (en) 2012-09-28 2015-02-03 Google Inc. Systems and methods for determining whether a driving environment has changed
US8948993B2 (en) 2013-03-08 2015-02-03 Richard Schulman Method and system for controlling the behavior of an occupant of a vehicle
US8954252B1 (en) 2012-09-27 2015-02-10 Google Inc. Pedestrian notifications
US8954217B1 (en) 2012-04-11 2015-02-10 Google Inc. Determining when to drive autonomously
US20150046080A1 (en) 2011-04-19 2015-02-12 Kees Wesselius Vehicle request management system having a central server
US20150066284A1 (en) 2013-09-05 2015-03-05 Ford Global Technologies, Llc Autonomous vehicle control for impaired driver
US20150088421A1 (en) 2013-09-26 2015-03-26 Google Inc. Controlling Navigation Software on a Portable Device from the Head Unit of a Vehicle
US8996224B1 (en) 2013-03-15 2015-03-31 Google Inc. Detecting that an autonomous vehicle is in a stuck condition
US9008890B1 (en) 2013-03-15 2015-04-14 Google Inc. Augmented trajectories for autonomous vehicles
US9019107B2 (en) 2013-06-19 2015-04-28 GM Global Technology Operations LLC Methods and apparatus for detection and reporting of vehicle operator impairment
US20150120504A1 (en) 2013-10-25 2015-04-30 Michael Todasco Systems and methods for completion of item delivery and transactions using a mobile beacon
US9026300B2 (en) 2012-11-06 2015-05-05 Google Inc. Methods and systems to aid autonomous vehicles driving through a lane merge
US20150149283A1 (en) 2003-05-28 2015-05-28 Eclipse Ip, Llc Notification Systems and Methods that Communicate Advertisements Based Upon Location and/or Personal Profiles
US20150148077A1 (en) 2013-11-25 2015-05-28 Agco Corporation Dynamic cooperative geofence
US20150185034A1 (en) 2007-01-12 2015-07-02 Raj V. Abhyanker Driverless vehicle commerce network and community
US20150199619A1 (en) 2012-08-07 2015-07-16 Hitachi, Ltd. Use-Assisting Tool for Autonomous Mobile Device, Operation Management Center, Operation System, and Autonomous Mobile Device
US9119038B2 (en) 2013-05-21 2015-08-25 Yopima Llc Systems and methods for comparative geofencing
US9120485B1 (en) 2012-09-14 2015-09-01 Google Inc. Methods and systems for smooth trajectory generation for a self-driving vehicle
US20150248689A1 (en) 2014-03-03 2015-09-03 Sunil Paul Systems and methods for providing transportation discounts
US9139133B2 (en) 2012-05-31 2015-09-22 GM Global Technology Operations LLC Vehicle collision warning system and method
US20150271290A1 (en) 2014-03-19 2015-09-24 Uber Technologies, Inc. Providing notifications to devices based on real-time conditions related to an on-demand service
US20150295949A1 (en) 2012-11-02 2015-10-15 University Of Washington Through Its Center For Commercialization Using Supplemental Encrypted Signals to Mitigate Man-in-the-Middle Attacks on Teleoperated Systems
US9194168B1 (en) 2014-05-23 2015-11-24 Google Inc. Unlock and authentication for autonomous vehicles
US20150339928A1 (en) 2015-08-12 2015-11-26 Madhusoodhan Ramanujam Using Autonomous Vehicles in a Taxi Service
US20150346727A1 (en) 2015-08-12 2015-12-03 Madhusoodhan Ramanujam Parking Autonomous Vehicles
US20150348221A1 (en) 2014-06-02 2015-12-03 Uber Technologies, Inc. Maintaining data for use with a transport service during connectivity loss between systems
US20160027307A1 (en) 2005-12-23 2016-01-28 Raj V. Abhyanker Short-term automobile rentals in a geo-spatial environment
US20160034828A1 (en) 2014-08-04 2016-02-04 Uber Technologies, Inc. Determining and providing predetermined location data points to service providers
US20160034845A1 (en) 2014-07-30 2016-02-04 Uber Technologies, Inc. Arranging a transport service for multiple users
US9262914B2 (en) 2011-09-29 2016-02-16 Tata Consultancy Services Limited Rogue vehicle detection
US9272713B1 (en) 2013-06-24 2016-03-01 Imperium Technologies LLC Compliance device, system and method for machine operation
US20160071056A1 (en) 2014-03-21 2016-03-10 United Parcel Service Of America, Inc. Programmatically executing time compressed delivery
US9290174B1 (en) 2014-10-23 2016-03-22 GM Global Technology Operations LLC Method and system for mitigating the effects of an impaired driver
US20160092976A1 (en) 2014-09-25 2016-03-31 2435603 Ontario Inc. Roving vehicle rental system and method
US20160116293A1 (en) 2014-10-22 2016-04-28 Myine Electronics, Inc. System and Method to Provide Valet Instructions for a Self-Driving Vehicle
US20160125735A1 (en) 2014-11-05 2016-05-05 Here Global B.V. Method and apparatus for providing access to autonomous vehicles based on user context
US20160129880A1 (en) 2014-11-07 2016-05-12 Ford Global Technologies, Llc Seat belt presenter fault indication
US20160140835A1 (en) 2014-11-18 2016-05-19 Smith Luby Holdings, LLC Emergency Service Provision with Destination-Specific Information
US20160182170A1 (en) 2014-06-10 2016-06-23 PB, Inc System Architectures and Methods for Radiobeacon Data Sharing
US20160187150A1 (en) 2014-12-30 2016-06-30 Ebay Inc. Determining and dispatching a ride-share vehicle
US20160209220A1 (en) 2014-01-21 2016-07-21 Tribal Rides, Inc. Method and system for anticipatory deployment of autonomously controlled vehicles
US20160209843A1 (en) 2015-01-15 2016-07-21 Nissan North America, Inc. Passenger docking location selection
US20160216130A1 (en) 2012-06-21 2016-07-28 Cellepathy Ltd. Enhanced navigation instruction
US20160227193A1 (en) 2013-03-15 2016-08-04 Uber Technologies, Inc. Methods, systems, and apparatus for multi-sensory stereo vision for robotics
US20160247106A1 (en) 2015-02-24 2016-08-25 Siemens Aktiengesellschaft Managing a fleet of autonomous electric vehicles for on-demand transportation and ancillary services to electrical grid
US20160247095A1 (en) 2015-02-24 2016-08-25 Addison Lee Limited Systems and Methods for Managing a Vehicle Sharing Facility
US20160247109A1 (en) 2015-02-24 2016-08-25 Addison Lee Limited Systems and Methods for Vehicle Resource Management
US9429947B1 (en) 2016-04-14 2016-08-30 Eric John Wengreen Self-driving vehicle systems and methods
US20160264021A1 (en) 2010-01-04 2016-09-15 Carla Gillett Autonomous Vehicle Seating System
US20160277560A1 (en) 2011-08-29 2016-09-22 Randal Gruberman System and method for remotely controlling features of wireless mobile devices
US20160301698A1 (en) 2013-12-23 2016-10-13 Hill-Rom Services, Inc. In-vehicle authorization for autonomous vehicles
US20160342934A1 (en) 2015-05-22 2016-11-24 Peter Michalik System and process for communicating between a drone and a handheld device
US9514623B1 (en) 2015-05-15 2016-12-06 Google Inc. Smoke detector chamber architecture and related methods using two different wavelengths of light
US20160360382A1 (en) 2015-05-27 2016-12-08 Apple Inc. Systems and Methods for Proactively Identifying and Surfacing Relevant Content on a Touch-Sensitive Device
US20160364823A1 (en) 2015-06-11 2016-12-15 Raymond Cao Systems and methods for on-demand transportation
US20160364812A1 (en) 2015-06-11 2016-12-15 Raymond Cao Systems and methods for on-demand transportation
US20160370194A1 (en) 2015-06-22 2016-12-22 Google Inc. Determining Pickup and Destination Locations for Autonomous Vehicles
US9527217B1 (en) 2015-07-27 2016-12-27 Westfield Labs Corporation Robotic systems and methods
US20170024393A1 (en) 2015-07-21 2017-01-26 Uber Technologies, Inc. User-based content filtering and ranking to facilitate on-demand services
US9562785B1 (en) * 2015-07-20 2017-02-07 Via Transportation, Inc. Continuously updatable computer-generated routes with continuously configurable virtual bus stops for passenger ride-sharing of a fleet of ride-sharing vehicles and computer transportation systems and computer-implemented methods for use thereof
US20170050321A1 (en) 2015-08-21 2017-02-23 Autodesk, Inc. Robot service platform
US20170068245A1 (en) 2014-03-03 2017-03-09 Inrix Inc. Driving profiles for autonomous vehicles
US20170075358A1 (en) 2014-05-06 2017-03-16 Huawei Technologies Co., Ltd. Self-driving car scheduling method, car scheduling server, and self-driving car
US20170090480A1 (en) 2015-09-24 2017-03-30 Uber Technologies, Inc. Autonomous vehicle operated with safety augmentation
US20170089715A1 (en) 2015-06-12 2017-03-30 Uber Technologies, Inc. System and method for providing contextual information for a location
US20170103490A1 (en) 2015-10-09 2017-04-13 Juno Lab, Inc. System to facilitate a correct identification of a service provider
US20170127215A1 (en) 2015-10-30 2017-05-04 Zemcar, Inc. Rules-Based Ride Security
US20170132540A1 (en) 2015-11-05 2017-05-11 Juno Lab, Inc. System for Identifying Events and Preemptively Navigating Drivers to Transport Passengers From the Events
US20170147951A1 (en) 2015-11-23 2017-05-25 Google Inc. Automatic booking of transportation based on context of a user of a computing device
US20170147959A1 (en) 2015-11-20 2017-05-25 Uber Technologies, Inc. Controlling autonomous vehicles in connection with transport services
US9685058B2 (en) 2015-05-15 2017-06-20 Google Inc. Smoke detector chamber
US20170213165A1 (en) 2016-01-26 2017-07-27 GM Global Technology Operations LLC Systems and methods for vehicle ride safety and security of person and property
US20170248949A1 (en) 2015-05-27 2017-08-31 Dov Moran Alerting predicted accidents between driverless cars
US20170277191A1 (en) 2016-03-24 2017-09-28 Waymo Llc Arranging passenger pickups for autonomous vehicles
US20170300053A1 (en) 2016-04-14 2017-10-19 Eric John Wengreen Self-driving vehicle systems and methods
US20170316516A1 (en) 2016-04-29 2017-11-02 GM Global Technology Operations LLC Systems and methods for managing a social autonomous taxi service
US20170313321A1 (en) 2016-04-28 2017-11-02 Honda Motor Co., Ltd. Vehicle control system, vehicle control method, and vehicle control program
US20170316621A1 (en) 2012-05-23 2017-11-02 Enterprise Holdings, Inc. Rental/Car-Share Vehicle Access and Management System and Method
US20170316533A1 (en) 2016-04-29 2017-11-02 GM Global Technology Operations LLC Personal safety and privacy features for passengers of an autonomous vehicle based transportation system
US20170327082A1 (en) 2016-05-12 2017-11-16 GM Global Technology Operations LLC End-to-end accommodation functionality for passengers of fully autonomous shared or taxi-service vehicles
US20170337437A1 (en) 2016-05-23 2017-11-23 Panasonic Automotive Systems Company Of America, Division Of Panasonic Corporation Of North America Trunk inventory detector
US20170344010A1 (en) 2016-05-27 2017-11-30 Uber Technologies, Inc. Facilitating rider pick-up for a self-driving vehicle
US20170352250A1 (en) 2016-06-01 2017-12-07 Tile, Inc. User Intervention Based on Tracking Device Location
US20170357973A1 (en) 2016-06-12 2017-12-14 Apple Inc. User interfaces for transactions
US20170363430A1 (en) 2014-12-05 2017-12-21 Apple Inc. Autonomous Navigation System
US20170372394A1 (en) 2013-03-15 2017-12-28 Home Depot Product Authority, Llc Facilitation of authorized in-store pickup
US9916703B2 (en) 2015-11-04 2018-03-13 Zoox, Inc. Calibration for autonomous vehicle operation
US20180075565A1 (en) 2016-09-13 2018-03-15 Ford Global Technologies, Llc Passenger validation systems and methods
US20180109934A1 (en) 2010-11-30 2018-04-19 Gary W. Grube Providing status of a user device during an adverse condition
US20180108103A1 (en) * 2016-01-27 2018-04-19 Beijing Didi Infinity Technology And Development Co., Ltd. Systems and methods for matching and displaying service request and available vehicles
US9953539B1 (en) * 2017-03-28 2018-04-24 Nec Corporation Method and system for providing demand-responsive dispatching of a fleet of transportation vehicles, and a mobility-activity processing module for providing a mobility trace database
US20180115924A1 (en) 2016-10-20 2018-04-26 Nokia Solutions And Networks Oy Dynamic Exchange Of Wireless Communication Services
US20180130161A1 (en) 2016-04-14 2018-05-10 Eric John Wengreen Self-driving vehicle systems and methods
US20180126960A1 (en) 2016-11-04 2018-05-10 Ford Global Technologies, Llc System and methods for assessing the interior of an autonomous vehicle
US20180137693A1 (en) 2016-11-15 2018-05-17 At&T Mobility Ii Llc Facilitation of smart communications hub to support driverless vehicles in 5g networks or other next generation networks
US20180157268A1 (en) 2016-12-06 2018-06-07 Delphi Technologies, Inc. Taxi client identification for automated vehicles
US20180156625A1 (en) 2016-12-06 2018-06-07 Delphi Technologies, Inc. Automated-vehicle pickup-location evaluation system
US20180191596A1 (en) 2016-12-30 2018-07-05 Google Inc. Selective sensor polling
US20180211541A1 (en) 2017-01-25 2018-07-26 Via Transportation, Inc. Prepositioning Empty Vehicles Based on Predicted Future Demand
US20180211540A1 (en) 2017-01-23 2018-07-26 Delphi Technologies, Inc. Automated vehicle transportation system for multiple-segment ground-transportation
US10036642B2 (en) 2015-12-08 2018-07-31 Uber Technologies, Inc. Automated vehicle communications system
US20180220189A1 (en) 2016-10-25 2018-08-02 725-1 Corporation Buffer Management for Video Data Telemetry
US20180225890A1 (en) 2017-02-03 2018-08-09 Ford Global Technologies, Llc System And Method For Assessing The Interior Of An Autonomous Vehicle
US20180225749A1 (en) 2013-07-26 2018-08-09 Edward J. Shoen Method and Apparatus for Mobile Rental of Vehicles
US10050760B2 (en) 2015-12-08 2018-08-14 Uber Technologies, Inc. Backend communications system for a fleet of autonomous vehicles
US10082789B1 (en) 2010-04-28 2018-09-25 Waymo Llc User interface for displaying internal state of autonomous driving system
US10093324B1 (en) 2010-04-28 2018-10-09 Waymo Llc User interface for displaying internal state of autonomous driving system
US10115029B1 (en) 2015-10-13 2018-10-30 Ambarella, Inc. Automobile video camera for the detection of children, people or pets left in a vehicle
US10127795B1 (en) 2017-12-31 2018-11-13 Lyft, Inc. Detecting and handling material left in vehicles by transportation requestors
US20180357907A1 (en) 2016-12-13 2018-12-13 drive.ai Inc. Method for dispatching a vehicle to a user's location

Patent Citations (171)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4212069A (en) 1976-08-31 1980-07-08 Baumann Dwight M Paratransit fare computation and dispatching method
US20080144944A1 (en) 1992-05-05 2008-06-19 Automotive Technologies International, Inc. Neural Network Systems for Vehicles
US20030195696A1 (en) 1993-05-18 2003-10-16 Jones M. Kelly Notification systems and methods with notifications based upon prior stop locations
US5769471A (en) 1995-09-04 1998-06-23 Aisin Seiki Kabushiki Kaisha Apparatus for unlocking a door lock for a vehicle
US5945919A (en) 1996-05-30 1999-08-31 Trimble Navigation Limited Dispatcher free vehicle allocation system
US5986420A (en) 1996-11-11 1999-11-16 Toyota Shatai Kabushiki Kaisha Apparatus for automatically opening and closing pop-up door of a vehicle
US5871063A (en) 1997-01-22 1999-02-16 Automotive Systems Laboratory, Inc. Seat belt latch sensor system
US5798695A (en) 1997-04-02 1998-08-25 Northrop Grumman Corporation Impaired operator detection and warning system employing analysis of operator control actions
US6011478A (en) 1997-05-08 2000-01-04 Nittan Company, Limited Smoke sensor and monitor control system
US6081088A (en) 1997-12-26 2000-06-27 Asmo Co., Ltd. Automatic opening/closing apparatus
US20040068354A1 (en) 1998-04-27 2004-04-08 Tabe Joseph A. Smart seatbelt control system
US5960523A (en) 1998-08-25 1999-10-05 Breed Automotive Technology, Inc. Seat belt buckle sensor
US20070132567A1 (en) 2000-03-02 2007-06-14 Donnelly Corporation Video mirror system suitable for use in a vehicle
US20020077876A1 (en) 2000-12-18 2002-06-20 O'meara Cian E. Allocation of location-based orders to mobile agents
US20020121291A1 (en) 2001-03-03 2002-09-05 Daum Wolfgang R.A. Method and device to clean the interior room of a car
US7093515B2 (en) 2002-10-09 2006-08-22 Nissan Motor Co., Ltd. Accelerator pedal device for a vehicle
US20040219933A1 (en) 2003-02-07 2004-11-04 Johnathan David Faith Transportation ordering system
US20150149283A1 (en) 2003-05-28 2015-05-28 Eclipse Ip, Llc Notification Systems and Methods that Communicate Advertisements Based Upon Location and/or Personal Profiles
US7298250B2 (en) 2003-08-29 2007-11-20 Mitsubishi Jidosha Kogyo Kabushiki Kaisha Seatbelt reminder system
US20070096447A1 (en) 2003-10-07 2007-05-03 Tabe Joseph A Smart seatbelt control system
US20080030906A1 (en) 2005-03-01 2008-02-07 Fujitsu Limited Magnetoresistive effect element and magnetic memory device
US7413357B2 (en) 2005-06-13 2008-08-19 Silverstate Safety Image Concealed camera
US20070198144A1 (en) 2005-10-21 2007-08-23 Norris William R Networked multi-role robotic vehicle
US20140172727A1 (en) 2005-12-23 2014-06-19 Raj V. Abhyanker Short-term automobile rentals in a geo-spatial environment
US20160027307A1 (en) 2005-12-23 2016-01-28 Raj V. Abhyanker Short-term automobile rentals in a geo-spatial environment
US20150185034A1 (en) 2007-01-12 2015-07-02 Raj V. Abhyanker Driverless vehicle commerce network and community
US9459622B2 (en) 2007-01-12 2016-10-04 Legalforce, Inc. Driverless vehicle commerce network and community
US7777619B2 (en) 2007-04-11 2010-08-17 Ford Global Technologies, Llc System and method for implementing active safety counter measures for an impaired driver
US8255124B2 (en) 2007-05-01 2012-08-28 Ronald Van Houten Enhanced seat belt/accelerator behavioral system
US20120158251A1 (en) 2007-05-01 2012-06-21 Ronald Van Houtan Enhanced seat belt/accelerator behavioral system
US7698078B2 (en) 2007-06-15 2010-04-13 Tesla Motors, Inc. Electric vehicle communication interface
US20110098017A1 (en) 2007-06-27 2011-04-28 Ford Global Technologies, Llc Method And System For Emergency Notification
US8180379B2 (en) 2007-06-28 2012-05-15 Apple Inc. Synchronizing mobile and vehicle devices
US20090140886A1 (en) 2007-12-03 2009-06-04 International Truck Intellectual Property Company, Llc Multiple geofence system for vehicles
US20090287367A1 (en) 2008-05-16 2009-11-19 Gm Global Technology Operations, Inc. Method and apparatus for driver control of a limited-ability autonomous vehicle
US20110059341A1 (en) 2008-06-12 2011-03-10 Junichi Matsumoto Electric vehicle
US7999701B1 (en) * 2008-06-26 2011-08-16 Bin Xu Transportation notification system
US8325025B2 (en) 2008-12-12 2012-12-04 Gordon*Howard Associates, Inc. Automated geo-fence boundary configuration and activation
US8078359B2 (en) 2009-10-05 2011-12-13 Tesla Motors, Inc. User configurable vehicle user interface
US20130132140A1 (en) 2009-12-04 2013-05-23 Uber Technologies, Inc. Determining a location related to on-demand services through use of portable computing devices
US20130246301A1 (en) 2009-12-04 2013-09-19 Uber Technologies, Inc. Providing user feedback for transport services through use of mobile devices
US20160264021A1 (en) 2010-01-04 2016-09-15 Carla Gillett Autonomous Vehicle Seating System
US10093324B1 (en) 2010-04-28 2018-10-09 Waymo Llc User interface for displaying internal state of autonomous driving system
US10082789B1 (en) 2010-04-28 2018-09-25 Waymo Llc User interface for displaying internal state of autonomous driving system
US20120009845A1 (en) 2010-07-07 2012-01-12 Juniper Holding Corp. Configurable location-aware toy capable of communicating with like toys and associated system infrastructure for communicating with such toys
US8874305B2 (en) 2010-10-05 2014-10-28 Google Inc. Diagnosis and repair for autonomous vehicles
US9120484B1 (en) 2010-10-05 2015-09-01 Google Inc. Modeling behavior based on observations of objects observed in a driving environment
US20120083960A1 (en) 2010-10-05 2012-04-05 Google Inc. System and method for predicting behaviors of detected objects
US8634980B1 (en) 2010-10-05 2014-01-21 Google Inc. Driving pattern recognition and safety control
US8965621B1 (en) 2010-10-05 2015-02-24 Google Inc. Driving pattern recognition and safety control
US20180109934A1 (en) 2010-11-30 2018-04-19 Gary W. Grube Providing status of a user device during an adverse condition
US20150046080A1 (en) 2011-04-19 2015-02-12 Kees Wesselius Vehicle request management system having a central server
US20140129132A1 (en) 2011-07-05 2014-05-08 Toyota Jidosha Kabushiki Kaisha Recommendation information provision system
US20160277560A1 (en) 2011-08-29 2016-09-22 Randal Gruberman System and method for remotely controlling features of wireless mobile devices
US20130085817A1 (en) 2011-09-29 2013-04-04 Michael Collins Pinkus Discount offer system and method for use with for hire vehicles
US9262914B2 (en) 2011-09-29 2016-02-16 Tata Consultancy Services Limited Rogue vehicle detection
US20130197674A1 (en) 2012-01-30 2013-08-01 Apple Inc. Automatic configuration of self-configurable environments
US20140350855A1 (en) 2012-02-28 2014-11-27 Google Inc. Systems and Methods for Providing Navigational Assistance to Reserved Parking Locations
US20130231824A1 (en) 2012-03-05 2013-09-05 Florida A&M University Artificial Intelligence Valet Systems and Methods
US8954217B1 (en) 2012-04-11 2015-02-10 Google Inc. Determining when to drive autonomously
US8700251B1 (en) 2012-04-13 2014-04-15 Google Inc. System and method for automatically detecting key behaviors by vehicles
US20170316621A1 (en) 2012-05-23 2017-11-02 Enterprise Holdings, Inc. Rental/Car-Share Vehicle Access and Management System and Method
US9139133B2 (en) 2012-05-31 2015-09-22 GM Global Technology Operations LLC Vehicle collision warning system and method
US20160216130A1 (en) 2012-06-21 2016-07-28 Cellepathy Ltd. Enhanced navigation instruction
US8433934B1 (en) 2012-06-28 2013-04-30 Google Inc. Saving battery on wireless connections on mobile devices using internal motion detection
US20150199619A1 (en) 2012-08-07 2015-07-16 Hitachi, Ltd. Use-Assisting Tool for Autonomous Mobile Device, Operation Management Center, Operation System, and Autonomous Mobile Device
US9120485B1 (en) 2012-09-14 2015-09-01 Google Inc. Methods and systems for smooth trajectory generation for a self-driving vehicle
US8954252B1 (en) 2012-09-27 2015-02-10 Google Inc. Pedestrian notifications
US8949016B1 (en) 2012-09-28 2015-02-03 Google Inc. Systems and methods for determining whether a driving environment has changed
US20150295949A1 (en) 2012-11-02 2015-10-15 University Of Washington Through Its Center For Commercialization Using Supplemental Encrypted Signals to Mitigate Man-in-the-Middle Attacks on Teleoperated Systems
US9026300B2 (en) 2012-11-06 2015-05-05 Google Inc. Methods and systems to aid autonomous vehicles driving through a lane merge
US20140129951A1 (en) 2012-11-08 2014-05-08 Uber Technologies, Inc. Providing on-demand services through use of portable computing devices
US8818608B2 (en) 2012-11-30 2014-08-26 Google Inc. Engaging and disengaging for autonomous driving
US8948993B2 (en) 2013-03-08 2015-02-03 Richard Schulman Method and system for controlling the behavior of an occupant of a vehicle
US20140316616A1 (en) 2013-03-11 2014-10-23 Airphrame, Inc. Unmanned aerial vehicle and methods for controlling same
US8996224B1 (en) 2013-03-15 2015-03-31 Google Inc. Detecting that an autonomous vehicle is in a stuck condition
US20160227193A1 (en) 2013-03-15 2016-08-04 Uber Technologies, Inc. Methods, systems, and apparatus for multi-sensory stereo vision for robotics
US9008890B1 (en) 2013-03-15 2015-04-14 Google Inc. Augmented trajectories for autonomous vehicles
US20170372394A1 (en) 2013-03-15 2017-12-28 Home Depot Product Authority, Llc Facilitation of authorized in-store pickup
US8849494B1 (en) 2013-03-15 2014-09-30 Google Inc. Data selection by an autonomous vehicle for trajectory modification
US20140336935A1 (en) 2013-05-07 2014-11-13 Google Inc. Methods and Systems for Detecting Weather Conditions Using Vehicle Onboard Sensors
US9119038B2 (en) 2013-05-21 2015-08-25 Yopima Llc Systems and methods for comparative geofencing
US9019107B2 (en) 2013-06-19 2015-04-28 GM Global Technology Operations LLC Methods and apparatus for detection and reporting of vehicle operator impairment
US9272713B1 (en) 2013-06-24 2016-03-01 Imperium Technologies LLC Compliance device, system and method for machine operation
US20150012833A1 (en) 2013-07-02 2015-01-08 Fortis Riders Corporation Mobile application using gestures to facilitate communication
US20180225749A1 (en) 2013-07-26 2018-08-09 Edward J. Shoen Method and Apparatus for Mobile Rental of Vehicles
US20150066284A1 (en) 2013-09-05 2015-03-05 Ford Global Technologies, Llc Autonomous vehicle control for impaired driver
US20150088421A1 (en) 2013-09-26 2015-03-26 Google Inc. Controlling Navigation Software on a Portable Device from the Head Unit of a Vehicle
US20150120504A1 (en) 2013-10-25 2015-04-30 Michael Todasco Systems and methods for completion of item delivery and transactions using a mobile beacon
US20150148077A1 (en) 2013-11-25 2015-05-28 Agco Corporation Dynamic cooperative geofence
US20160301698A1 (en) 2013-12-23 2016-10-13 Hill-Rom Services, Inc. In-vehicle authorization for autonomous vehicles
US20160209220A1 (en) 2014-01-21 2016-07-21 Tribal Rides, Inc. Method and system for anticipatory deployment of autonomously controlled vehicles
US20150248689A1 (en) 2014-03-03 2015-09-03 Sunil Paul Systems and methods for providing transportation discounts
US20170068245A1 (en) 2014-03-03 2017-03-09 Inrix Inc. Driving profiles for autonomous vehicles
US20150271290A1 (en) 2014-03-19 2015-09-24 Uber Technologies, Inc. Providing notifications to devices based on real-time conditions related to an on-demand service
US20160071056A1 (en) 2014-03-21 2016-03-10 United Parcel Service Of America, Inc. Programmatically executing time compressed delivery
US20170075358A1 (en) 2014-05-06 2017-03-16 Huawei Technologies Co., Ltd. Self-driving car scheduling method, car scheduling server, and self-driving car
US9194168B1 (en) 2014-05-23 2015-11-24 Google Inc. Unlock and authentication for autonomous vehicles
US20150348221A1 (en) 2014-06-02 2015-12-03 Uber Technologies, Inc. Maintaining data for use with a transport service during connectivity loss between systems
US20160182170A1 (en) 2014-06-10 2016-06-23 PB, Inc System Architectures and Methods for Radiobeacon Data Sharing
US20160034845A1 (en) 2014-07-30 2016-02-04 Uber Technologies, Inc. Arranging a transport service for multiple users
US20160034828A1 (en) 2014-08-04 2016-02-04 Uber Technologies, Inc. Determining and providing predetermined location data points to service providers
US20160092976A1 (en) 2014-09-25 2016-03-31 2435603 Ontario Inc. Roving vehicle rental system and method
US20160116293A1 (en) 2014-10-22 2016-04-28 Myine Electronics, Inc. System and Method to Provide Valet Instructions for a Self-Driving Vehicle
US9290174B1 (en) 2014-10-23 2016-03-22 GM Global Technology Operations LLC Method and system for mitigating the effects of an impaired driver
US20160125735A1 (en) 2014-11-05 2016-05-05 Here Global B.V. Method and apparatus for providing access to autonomous vehicles based on user context
US20160129880A1 (en) 2014-11-07 2016-05-12 Ford Global Technologies, Llc Seat belt presenter fault indication
US20160140835A1 (en) 2014-11-18 2016-05-19 Smith Luby Holdings, LLC Emergency Service Provision with Destination-Specific Information
US20170363430A1 (en) 2014-12-05 2017-12-21 Apple Inc. Autonomous Navigation System
US20160187150A1 (en) 2014-12-30 2016-06-30 Ebay Inc. Determining and dispatching a ride-share vehicle
US20160209843A1 (en) 2015-01-15 2016-07-21 Nissan North America, Inc. Passenger docking location selection
US20160247095A1 (en) 2015-02-24 2016-08-25 Addison Lee Limited Systems and Methods for Managing a Vehicle Sharing Facility
US20160247106A1 (en) 2015-02-24 2016-08-25 Siemens Aktiengesellschaft Managing a fleet of autonomous electric vehicles for on-demand transportation and ancillary services to electrical grid
US20160247109A1 (en) 2015-02-24 2016-08-25 Addison Lee Limited Systems and Methods for Vehicle Resource Management
US9685058B2 (en) 2015-05-15 2017-06-20 Google Inc. Smoke detector chamber
US9514623B1 (en) 2015-05-15 2016-12-06 Google Inc. Smoke detector chamber architecture and related methods using two different wavelengths of light
US20160342934A1 (en) 2015-05-22 2016-11-24 Peter Michalik System and process for communicating between a drone and a handheld device
US20170248949A1 (en) 2015-05-27 2017-08-31 Dov Moran Alerting predicted accidents between driverless cars
US20160360382A1 (en) 2015-05-27 2016-12-08 Apple Inc. Systems and Methods for Proactively Identifying and Surfacing Relevant Content on a Touch-Sensitive Device
US20160364812A1 (en) 2015-06-11 2016-12-15 Raymond Cao Systems and methods for on-demand transportation
US20160364823A1 (en) 2015-06-11 2016-12-15 Raymond Cao Systems and methods for on-demand transportation
US20170089715A1 (en) 2015-06-12 2017-03-30 Uber Technologies, Inc. System and method for providing contextual information for a location
US9733096B2 (en) 2015-06-22 2017-08-15 Waymo Llc Determining pickup and destination locations for autonomous vehicles
US20160370194A1 (en) 2015-06-22 2016-12-22 Google Inc. Determining Pickup and Destination Locations for Autonomous Vehicles
US9562785B1 (en) * 2015-07-20 2017-02-07 Via Transportation, Inc. Continuously updatable computer-generated routes with continuously configurable virtual bus stops for passenger ride-sharing of a fleet of ride-sharing vehicles and computer transportation systems and computer-implemented methods for use thereof
US20170024393A1 (en) 2015-07-21 2017-01-26 Uber Technologies, Inc. User-based content filtering and ranking to facilitate on-demand services
US9527217B1 (en) 2015-07-27 2016-12-27 Westfield Labs Corporation Robotic systems and methods
US20150339928A1 (en) 2015-08-12 2015-11-26 Madhusoodhan Ramanujam Using Autonomous Vehicles in a Taxi Service
US20150346727A1 (en) 2015-08-12 2015-12-03 Madhusoodhan Ramanujam Parking Autonomous Vehicles
US20170050321A1 (en) 2015-08-21 2017-02-23 Autodesk, Inc. Robot service platform
US20170090480A1 (en) 2015-09-24 2017-03-30 Uber Technologies, Inc. Autonomous vehicle operated with safety augmentation
US20170103490A1 (en) 2015-10-09 2017-04-13 Juno Lab, Inc. System to facilitate a correct identification of a service provider
US10115029B1 (en) 2015-10-13 2018-10-30 Ambarella, Inc. Automobile video camera for the detection of children, people or pets left in a vehicle
US20170127215A1 (en) 2015-10-30 2017-05-04 Zemcar, Inc. Rules-Based Ride Security
US9916703B2 (en) 2015-11-04 2018-03-13 Zoox, Inc. Calibration for autonomous vehicle operation
US20170132540A1 (en) 2015-11-05 2017-05-11 Juno Lab, Inc. System for Identifying Events and Preemptively Navigating Drivers to Transport Passengers From the Events
US20170147959A1 (en) 2015-11-20 2017-05-25 Uber Technologies, Inc. Controlling autonomous vehicles in connection with transport services
US9953283B2 (en) 2015-11-20 2018-04-24 Uber Technologies, Inc. Controlling autonomous vehicles in connection with transport services
US20170147951A1 (en) 2015-11-23 2017-05-25 Google Inc. Automatic booking of transportation based on context of a user of a computing device
US10050760B2 (en) 2015-12-08 2018-08-14 Uber Technologies, Inc. Backend communications system for a fleet of autonomous vehicles
US10036642B2 (en) 2015-12-08 2018-07-31 Uber Technologies, Inc. Automated vehicle communications system
US20170213165A1 (en) 2016-01-26 2017-07-27 GM Global Technology Operations LLC Systems and methods for vehicle ride safety and security of person and property
US20180108103A1 (en) * 2016-01-27 2018-04-19 Beijing Didi Infinity Technology And Development Co., Ltd. Systems and methods for matching and displaying service request and available vehicles
US20170277191A1 (en) 2016-03-24 2017-09-28 Waymo Llc Arranging passenger pickups for autonomous vehicles
US9429947B1 (en) 2016-04-14 2016-08-30 Eric John Wengreen Self-driving vehicle systems and methods
US20170300053A1 (en) 2016-04-14 2017-10-19 Eric John Wengreen Self-driving vehicle systems and methods
US9646356B1 (en) 2016-04-14 2017-05-09 Wesley Edward Schwie Self-driving vehicle systems and methods
US9915949B2 (en) 2016-04-14 2018-03-13 Wesley Edward Schwie Self-driving vehicle systems and methods
US20180130161A1 (en) 2016-04-14 2018-05-10 Eric John Wengreen Self-driving vehicle systems and methods
US20170313321A1 (en) 2016-04-28 2017-11-02 Honda Motor Co., Ltd. Vehicle control system, vehicle control method, and vehicle control program
US20170316516A1 (en) 2016-04-29 2017-11-02 GM Global Technology Operations LLC Systems and methods for managing a social autonomous taxi service
US20170316533A1 (en) 2016-04-29 2017-11-02 GM Global Technology Operations LLC Personal safety and privacy features for passengers of an autonomous vehicle based transportation system
US20170327082A1 (en) 2016-05-12 2017-11-16 GM Global Technology Operations LLC End-to-end accommodation functionality for passengers of fully autonomous shared or taxi-service vehicles
US20170337437A1 (en) 2016-05-23 2017-11-23 Panasonic Automotive Systems Company Of America, Division Of Panasonic Corporation Of North America Trunk inventory detector
US20170344010A1 (en) 2016-05-27 2017-11-30 Uber Technologies, Inc. Facilitating rider pick-up for a self-driving vehicle
US20170352250A1 (en) 2016-06-01 2017-12-07 Tile, Inc. User Intervention Based on Tracking Device Location
US20170357973A1 (en) 2016-06-12 2017-12-14 Apple Inc. User interfaces for transactions
US20180075565A1 (en) 2016-09-13 2018-03-15 Ford Global Technologies, Llc Passenger validation systems and methods
US20180115924A1 (en) 2016-10-20 2018-04-26 Nokia Solutions And Networks Oy Dynamic Exchange Of Wireless Communication Services
US20180220189A1 (en) 2016-10-25 2018-08-02 725-1 Corporation Buffer Management for Video Data Telemetry
US20180126960A1 (en) 2016-11-04 2018-05-10 Ford Global Technologies, Llc System and methods for assessing the interior of an autonomous vehicle
US20180137693A1 (en) 2016-11-15 2018-05-17 At&T Mobility Ii Llc Facilitation of smart communications hub to support driverless vehicles in 5g networks or other next generation networks
US20180156625A1 (en) 2016-12-06 2018-06-07 Delphi Technologies, Inc. Automated-vehicle pickup-location evaluation system
US20180157268A1 (en) 2016-12-06 2018-06-07 Delphi Technologies, Inc. Taxi client identification for automated vehicles
US20180357907A1 (en) 2016-12-13 2018-12-13 drive.ai Inc. Method for dispatching a vehicle to a user's location
US20180191596A1 (en) 2016-12-30 2018-07-05 Google Inc. Selective sensor polling
US20180211540A1 (en) 2017-01-23 2018-07-26 Delphi Technologies, Inc. Automated vehicle transportation system for multiple-segment ground-transportation
US20180211541A1 (en) 2017-01-25 2018-07-26 Via Transportation, Inc. Prepositioning Empty Vehicles Based on Predicted Future Demand
US20180225890A1 (en) 2017-02-03 2018-08-09 Ford Global Technologies, Llc System And Method For Assessing The Interior Of An Autonomous Vehicle
US9953539B1 (en) * 2017-03-28 2018-04-24 Nec Corporation Method and system for providing demand-responsive dispatching of a fleet of transportation vehicles, and a mobility-activity processing module for providing a mobility trace database
US10127795B1 (en) 2017-12-31 2018-11-13 Lyft, Inc. Detecting and handling material left in vehicles by transportation requestors

Non-Patent Citations (49)

* Cited by examiner, † Cited by third party
Title
Assisted GPS-Downloaded on Nov. 19, 2018 from lifewire.com/assisted-gps-1683306.
Assisted GPS—Downloaded on Nov. 19, 2018 from lifewire.com/assisted-gps-1683306.
BMW Heads Up Display-Online prior to Apr. 13, 2016 at www.autotrader.com/car-news/full-color-heads-up-display-to-debut-on-new-3-series-132586.
BMW Heads Up Display—Online prior to Apr. 13, 2016 at www.autotrader.com/car-news/full-color-heads-up-display-to-debut-on-new-3-series-132586.
Explain That Stuff: Smoke Detectors-Downloaded on Sep. 28, 2018 from www.explainthatstuff.com/smokedetector.html.
Explain That Stuff: Smoke Detectors—Downloaded on Sep. 28, 2018 from www.explainthatstuff.com/smokedetector.html.
Google Self-Driving Vehicle-Online prior to Apr. 13, 2016 at www.google.com/selfdrivingcar/.
Google Self-Driving Vehicle—Online prior to Apr. 13, 2016 at www.google.com/selfdrivingcar/.
How GPS Works-Downloaded on Nov. 19, 2018 from lifewire.com/iphone-gps-set-up-1683393.
How GPS Works—Downloaded on Nov. 19, 2018 from lifewire.com/iphone-gps-set-up-1683393.
How Police Visually Detect Drunk Drivers-Downloaded on Oct. 19, 2018 from thelaw.com/law/how-police-visually-detect-drunk-drivers.185.
How Police Visually Detect Drunk Drivers—Downloaded on Oct. 19, 2018 from thelaw.com/law/how-police-visually-detect-drunk-drivers.185.
Indoor Positioning System-Downloaded on Nov. 19, 2018 from en.wikipedia.org/wiki/Indoor_positioning_system.
Indoor Positioning System—Downloaded on Nov. 19, 2018 from en.wikipedia.org/wiki/Indoor_positioning_system.
Lidar-Downloaded on Oct. 24, 2018 from en.wikipedia.org/wiki/Lidar.
Lidar—Downloaded on Oct. 24, 2018 from en.wikipedia.org/wiki/Lidar.
LTE-Downloaded on Nov. 27, 2018 from en.wikipedia.org/wiki/LTE_(telecommunication).
LTE—Downloaded on Nov. 27, 2018 from en.wikipedia.org/wiki/LTE_(telecommunication).
Mark Harris, Uber Could Be First to Test Completely Driverless Cars in Public, Sep. 14, 2015, IEEE Spectrum, http://spectrum.ieee.org/cars-that-think/transportation/self-driving/uber-could-be-first-to-test-completely-driverless-cars-in-public.
Nest: Split-Spectrum White Paper-Downloaded on Oct. 1, 2018 from nest.com/support/images/misc-assets/Split-Spectrum-Sensor-White-Paper.pdf.
Nest: Split-Spectrum White Paper—Downloaded on Oct. 1, 2018 from nest.com/support/images/misc-assets/Split-Spectrum-Sensor-White-Paper.pdf.
Nittan: EV-DP Smoke Detector-Downloaded on Sep. 28, 2018 from nittan.co.uk/products/products/ev/ev-dp.
Nittan: EV-DP Smoke Detector—Downloaded on Sep. 28, 2018 from nittan.co.uk/products/products/ev/ev-dp.
OTDOA-Downloaded on Nov. 27, 2018 from en.wikipedia.org/wiki/OTDOA.
OTDOA—Downloaded on Nov. 27, 2018 from en.wikipedia.org/wiki/OTDOA.
Ping for Beginners-Downloaded on Jan. 30, 2019 from https://social.technet.microsoft.com/wiki/contents/articles/30110.ping-for-beginners.aspx.
Ping for Beginners—Downloaded on Jan. 30, 2019 from https://social.technet.microsoft.com/wiki/contents/articles/30110.ping-for-beginners.aspx.
Radar-Downloaded on Oct. 24, 2018 from en.wikipedia.org/wiki/Radar.
Radar—Downloaded on Oct. 24, 2018 from en.wikipedia.org/wiki/Radar.
Ramsey et al., GM, Lyft to Test Self-Driving Electric Taxis, May 5, 2016, The Wall Street Journal, http://www.wsj.com/articles/gm-lyft-to-test-self-driving-electric-taxis-1462460094, pp. 1-4.
Raspberry Pi: How can I detect the direction of a sound-Online prior to Apr. 13, 2016 at www.quora.com/Raspberry-Pi-1/How-can-I-detect-the-direction-of-a-sound.
Raspberry Pi: How can I detect the direction of a sound—Online prior to Apr. 13, 2016 at www.quora.com/Raspberry-Pi-1/How-can-I-detect-the-direction-of-a-sound.
Self-Driving Cars Go Public; Uber Offers Rides in Pittsburgh-Downloaded on Aug. 19, 2016 from www.yahoo.com/news/uber-autonomous-cars-haul-people-125127470.html?ref=gs.
Self-Driving Cars Go Public; Uber Offers Rides in Pittsburgh—Downloaded on Aug. 19, 2016 from www.yahoo.com/news/uber-autonomous-cars-haul-people-125127470.html?ref=gs.
Tesla Model S Software Version 7-Autopilot-Online prior to Apr. 13, 2016 at www.teslamotors.com/presskit/autopilot.
Tesla Model S Software Version 7—Autopilot—Online prior to Apr. 13, 2016 at www.teslamotors.com/presskit/autopilot.
Testa Autopilot-Online prior to Apr. 13, 2016 at www.technologyreview.com/s/600772/10-breakthrough-technologies-2016-tesla-autopilot/.
Testa Autopilot—Online prior to Apr. 13, 2016 at www.technologyreview.com/s/600772/10-breakthrough-technologies-2016-tesla-autopilot/.
Uber Details-Online prior to Apr. 13, 2016 at www.wikihow.com/Use-Uber.
Uber Details—Online prior to Apr. 13, 2016 at www.wikihow.com/Use-Uber.
Velodyne VLS-128 LiDAR Sensor-Downloaded on Oct. 22, 2018 from velodynelidar.com/vls-128.html.
Velodyne VLS-128 LiDAR Sensor—Downloaded on Oct. 22, 2018 from velodynelidar.com/vls-128.html.
Waymo's Suite of Custom-Built, Self-Driving Hardware-Downloaded on Oct. 22, 2018 from medium.com/waymo/introducing-waymos-suite-of-custom-built-self-driving-hardware-c47d1714563.
Waymo's Suite of Custom-Built, Self-Driving Hardware—Downloaded on Oct. 22, 2018 from medium.com/waymo/introducing-waymos-suite-of-custom-built-self-driving-hardware-c47d1714563.
Wikipedia: Biometric Device-Downloaded on Aug. 19, 2016 from en.wikipedia.org/wiki/Biometric_device.
Wikipedia: Biometric Device—Downloaded on Aug. 19, 2016 from en.wikipedia.org/wiki/Biometric_device.
Wikipedia: Rain Sensor-Downloaded on Sep. 28, 2018 from en.wikipedia.org/wiki/Rain_sensor.
Wikipedia: Rain Sensor—Downloaded on Sep. 28, 2018 from en.wikipedia.org/wiki/Rain_sensor.
Zach Epstein, You'll be riding in self-driving cars as soon as next year, May 6, 2016, BGR.com, http://bgr.com/2016105'06/lyfl-self-driving-cars-2017/, pp. 1-5.

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